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K-12 schools are the largest energy consumers in the public sector, with their HVAC energy consumption representing the largest portion of their total energy use. While transitioning these schools to grid-interactive HVAC system operation through advanced controls offers significant financial and environmental benefits, and model predictive control (MPC) has been identified as a promising solution to achieve that, very few MPCs are affordable and have been deployed in K-12 schools. This situation raises concerns about the unclear real-world benefits of MPC technology among facility managers and industries. To address this gap, this paper presents a low-cost MPC solution that requires minimal control infrastructure costs and a unique field demonstration at a K-12 school, conducted for both cooling and heating seasons. This work adopted a previously developed MPC and extended it for use in the school application. The MPC aims to coordinate multiple packaged units to eliminate unnecessary peaks and shift cooling or heating loads in response to grid signals based on load conditions, while maintaining thermostat temperatures within school-defined bounds. Throughout the field tests, the MPC achieved a 24% reduction in peak demand during the cooling season and shifted cooling or heating loads by up to 16% in response to the school's utility tariff, considering load conditions, while also allowing end-users to override thermostat setpoints. The paper also discusses the limitations of this study and future research directions for better performance of the MPC at K-12 schools. • First MPC demonstration in K-12 school buildings for peak demand reduction and load shifting. • Experimentally demonstrated a scalable MPC for multiple RTUs without hardware retrofit. • Hierarchical MPC is improved in consideration of practical challenges such as user's override and poor data resolutions. • Achieved 24% peak demand reduction and showed 16% of load shifting potential. • Share lessons learned during the demonstration of practical challenges. [ABSTRACT FROM AUTHOR]

As hot days are getting hotter and more frequent, urban dwelling is expected to increase cooling energy use in current and future climate. The applicability of dynamic building simulation in estimating cooling loads of a city's housing stock can be limited due to lack of fine-grained on-site current and future weather inputs. For predicative modelling of residential cooling energy demand to aid a city's energy supply planning resilient to excessive heat conditions, it requires cooling energy demand projection based on a relational account of (1) the thermal-environmental interaction between housing stocks and urban microclimate conditions, (2) the city dwellers' cooling energy use behaviour, and (3) the city's climate projections. In this paper, we introduce an 'archetype-in-neighbourhood' framework to meet these requirements. Combining empirical urban data modelling and EngeryPlus model calibration, this framework was developed to obtain statistically a maximal cooling energy demand model of a city's housing stock during yearly hottest periods. We applied the framework to multiple datasets selected from Seoul's open urban data sources for the period of 2014–2017 (2014 being the earliest year of data availability, 2017 being the end of the study period), including metered electricity use data of 659 apartment buildings (51,351 households) sampled from 18 city districts. The results show that maximal month cooling energy demand (MMCD, kWh/m2) of Seoul's housing stock can be expressed as a regression function of two determinants: (1) the city's average outdoor temperature during the hottest month period (T ex , °C), and (2) estimated indoor cooling temperature set-point (T in , °C) of the city' housing stock during the same period. Through a k -fold (k = 4) validation, the current regression model (2014–17) was evaluated to have an overall coefficient of determination R2 =0.969. Assuming no housing stock renovation, we applied the model to generate scenarios of maximal month cooling demand in future years according to some of the highest summer temperatures projected for Seoul (RCP8.5 2045, RCP4.5 2047, MM5 2071–2100). We conclude this paper with a brief discussion of the implication for cooling energy supply planning and further work to extend the applicability of this new framework to housing stock adaptation planning and design. [ABSTRACT FROM AUTHOR]

Dynamic cooling and heating load forecasting of heating, ventilation and air conditioning (HVAC) systems is a basis for optimizing the operation of HVAC systems and can contribute to achieving the effective management for the HVAC systems. This paper proposes a load forecasting method for office buildings based on artificial intelligence and regression analysis, including wavelet transform, support vector machines (SVM), and partial least squares regression (PLS). An office building located in Tianjin, China is taken as the building case study to validate the proposed model. For selecting the input variables, the methods of sensitivity analysis and correlation analysis are used. The results of different prediction horizons, mainly including 1 h ahead, 2 h ahead, 3 h ahead and 24 h ahead forecasting, are finally provided. In order to illustrate the accuracy improvement of the proposed model, the other three models are built to compare with the proposed model. Further, the influence of weather forecast precision on the proposed model is analyzed in this paper. The results indicate that the proposed method can realize dynamic load forecasting with high accuracy for different time horizons. [ABSTRACT FROM AUTHOR]

Due to vertically thermal stratification in indoor spaces, the cooling load calculation is always a challenge in the design of stratified air distribution systems (STRAD), which in turn is crucial to determine the supply air flow rate and the cooling load to be removed by the air conditioning system. In this paper, several cooling load calculation methods, developed in the past 20 years and focused on STRAD systems, were briefly reviewed. It attempts to clarify these methods in terms of their advantages, limitations and suitable ranges of applications. Furthermore, series of numerical simulations with a total number of 56 cases were conducted, and the applications of a novel cooling load calculation method in STRAD systems were investigated. The reliability of the method was validated for STRAD systems with separated locations of return and exhaust grilles, when adopted in three typical spaces with different building heights, namely a small office, a large terraced classroom and a terminal building. Databases of effective cooling load factors ( ECLF i ) for different heat sources distributed in the three spaces were obtained and presented, which can be conveniently used to calculate cooling loads. In addition, the influences of return grill height on the occupied zone cooling load as well as the energy saving potentials of STRAD systems were also clarified. The results presented in this paper are helpful for the design and optimization of STRAD systems. [ABSTRACT FROM AUTHOR]

Summer peak electric demand of commercial buildings and districts is typically driven by the cooling load. This paper presents a novel approach to holistically optimize both the air- and water-side of the HVAC system in existing commercial districts. Therefore, this paper is different from the existing literature in its focus on integrated optimization of both air- and water-side of the HVAC system in a district scale as opposed to only airside or waterside. Methodologies proposed in this paper for integrated optimization of air- and water-side are brand-agnostic and their capabilities are demonstrated using a chilled water plant at Colorado School of Mines' campus. Based on the results for the test district, Mines' integrated air- and water-side optimizations of the HVAC system reduces its peak electric demand by 15% and 13%, respectively, for August 19 and 20, 2019. The air- and water-side optimization of the HVAC system resulted in greater savings than that achieved with only the waterside demand optimization method discussed in authors' previous paper focused on existing commercial districts. [ABSTRACT FROM AUTHOR]

Traditional prediction methods of building cooling load fail to consider load changes caused by the time factor, which results in the failure of the best predicting effect and portability. To explore a more accurate cooling load predicting approach, in this paper, a new hybrid model based on decoupled weight decay Adam optimized improved LSTM neural network, denoted as DwdAdam-ILSTM, is proposed. First, the cross-correlation function is used to extract features with a high degree as the input data. Then, the decoupling weight decay adaptive moment estimation (DwdAdam) optimization algorithm is utilized to optimize the parameters of the improved long short term memory (ILSTM) prediction model. Next, the ILSTM is employed to establish a prediction model. Finally, to verify the validity and prediction accuracy of the proposed model, this paper selects the hourly cooling load data of a large commercial building in Xi'an as the research sample. Results of the experiment show that the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), Variation Coefficient of Root Mean Square Error (CV-RMSE) and Range of Relative Errors (E-R) of DwdAdam-ILSTM predicting the cooling load are 2.2%, 2.74, 5.2%, 0.061, 0.216. Compared with three version variant LSTM neural networks, the MSE and E-R values of this model are decreased by 74%-53%, 42%-49%, and 69%-64%, which has better performance in accuracy and stability. It testifies that the proposed strategy is reliable and effective. Additionally, this model can provide important guidance for daily energy management and multi-energy scheduling of buildings, which is helpful to the application of cooling load prediction. [ABSTRACT FROM AUTHOR]

This paper summarises the results of a study to identify and quantify the potential impact of measures and policies to reduce the energy consumption of air conditioning in European countries over a 10-year period. The market penetration of air conditioning in Europe has increased steadily over several decades. On a “business as usual” basis, its energy consumption would increase by over 50% by 2020. Measures to restrict this increase are therefore important, but the savings potential straddles several overlapping areas which are subject to different constraints including those imposed by the replacement rates of systems and products, and the refurbishment rates of existing buildings. This work reported in this paper is based on the use of information from a variety of sources in a highly disaggregated model to assess the “realisable” savings over a 10-year period, taking into account relevant constraints. The paper summarises results at a European level, focussing on those cases that generate the greatest realisable savings. Savings are possible in three areas: improved equipment and system efficiency, reduced cooling loads, and more effective operation. The cases that offer the largest savings provide the basis for recommendations for policy measures, often using existing policy instruments. In addition, areas where further work is needed are identified. More detailed results and information about the study and additional recommendations can be found at http://www.bre.co.uk/searchresults.jsp?category=5&q=energy+management [1] . [ABSTRACT FROM AUTHOR]

The energy consumption necessary to maintain thermal comfort in buildings remains a significant and partially unjustified part, accounting for about 30–40% of the total energy consumption. Despite the increasingly pressing regulations in the energy sector (EPBD recast), the quality of the internal environment remains one of the critical parameters on which to pay more attention in view of a consistent evaluation of the Nzeb building. The windowed components are often designed with thermal insulation performances higher than the minimum requirements but still they lack in design of the incident solar radiation control, which is a particularly delicate topic for indoor comfort and more generally for a higher energy efficiency in buildings. If we think of the numerous variables of influence (typology, orientation, geographical location, etc.), the possibilities to associate energy saving, lighting control and users' comfort are inevitably entrusted to dynamic systems integrated into the glass. In this paper authors present the results related to the experimental characterization of two identical test rooms with different typologies of windows. One room is furnished with a traditional high thermal performance glass and the other, with an electrochromic glass suitably controlled by remote. The operative temperature measured in both test rooms have been utilized to verify the simulation model developed to assess the energy saving achievable in a Mediterranean climate, taking properly into account the hourly variations of the climate and the actions on the shading regulation systems. [ABSTRACT FROM AUTHOR]

• Thermal performance of energy pile systems was evaluated by performing field tests and numerical simulations. • The effect of thermal interference on the thermal behavior of energy piles was investigated. • A design chart for considering the thermal interference effect depending on the denseness of heat exchange pipes was provided. In general, the design of closed-loop vertical ground heat exchangers (GHEXs) is to determine the required borehole length for providing the heating and cooling load of a target building. However, the same design procedure cannot be applied to the cast-in-place energy pile since the configuration of the heat exchange pipe inside the pile is too varied and the size of the borehole is relatively large. In this paper, the effect of configuration and denseness of heat exchange pipe inside the energy pile was experimentally and numerically evaluated comparing with conventional GHEXs. First, field experiments were performed for six cast-in-place energy piles constructed in a test bed. The results showed that the cast-in-place energy piles could provide higher thermal performance per unit borehole length than the other conventional types of GHEXs by increasing the heat exchange area along with much longer heat exchange pipes. However, the over-tight layout diminished the improvement of thermal performance due to thermal interference between each pipe loop. In addition, the long-term thermal behavior of energy piles and changes in ground temperature were estimated with numerical simulations. With the simulation results, a design chart was provided for evaluating the effect of thermal interference on energy pile performance. [ABSTRACT FROM AUTHOR]

For cooling equipment management and scheduling optimization, accurate building cooling load forecasting technology is crucial. Currently, the physics-based forecasting models are too complex to achieve, and existing shallow-machine and deep learning algorithms are difficult to capture and retain sequential information from historical building cooling loads, leading to insufficient prediction accuracy. This paper considered the dependency relationship between time-series information in load data and proposed a building load prediction model based on a transformer network to improve the accuracy of building load prediction. This encoder-decoder block-based model can encode and decode all input data, capture sequence information from mapping vectors with user-defined dimensions, and learn important features through the Attention mechanism. In addition, input features were analyzed to verify the importance of each input feature, and to explaine the reasons for the impact of used features on the TRN-based model. Finally, the performance of the proposed model is evaluated using real data from an office building. Compared with other existing methods, the proposed model has the best prediction accuracy (RMSE, MAE, R2 were 0.01, 0.03, and 0.98, respectively), and maintained the best predictive stability over a longer time (uncertainty ranged from −11% to + 11%). The results show that the proposed method can support the development and optimal operation of energy-saving HVAC systems, thereby lowing power consumption. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a method to determine the optimum capacity of distributed generation technologies for buildings. The Combined Heat and Power (CHP) unit has both absorption and electric chiller for cooling loads, and a supplementary boiler for heating loads. The demand is uncertain in the model. The underlying uncertainty is modeled using probabilistic theory. In this method, the expected value of the objective function is evaluated using numerical approaches. A simulation based optimization method is presented which can estimate the cost of system operation and find the appropriate design by means of stochastic programming. The effect of minimum economic turn-down of the facilities is also discussed in this paper. The optimum capacity of distributed generation technologies has a great effect on the cost of the building. Therefore, the proposed method is applied for a hospital in Iran as a case study. The results show that the optimum capacities of CHP and absorption chiller decreases and the capacities of the electric chiller and boiler rise by an increase in demand uncertainty. In addition, the appropriate minimum economic turn-down of facilities has a considerable effect on the overall system cost. [Copyright &y& Elsevier]

Chillers are responsible for nearly 30–40% of the energy consumption of an HVAC system. To enhance the efficiency of HVAC systems, it is necessary to optimize the operation of chillers. Conventional chiller operation strategies are mostly intended to save energy by suitably distributing the chiller cooling load in accordance with the measured cooling system data, which is typically considered noise-free. In other words, the uncertainty of the cooling system measurement and the necessity of customized optimization—as different buildings require different tradeoffs between energy-saving and indoor comfort—are usually not considered. This paper proposes a stochastic optimized chiller operation strategy based on multi-objective optimization and measurement uncertainty. The strategy consists of five steps: (1) Quantify the indoor comfort utility and energy consumption utility by using utility functions; (2) integrate these two utilities into one comprehensive utility using user-defined weights; (3) specify the measurement uncertainty distribution in the cooling system; (4) traverse each possible operation plan, and calculate the mean value (i.e., mathematical expectation) of its corresponding comprehensive utility (expected utility, EU); and (5) determine the optimal operation plan to be the one corresponding to the maximum EU value. The performance of the proposed strategy is validated by establishing a cooling system model on TRNSYS and comparing the findings with those of two conventional chiller operation strategies commonly adopted in Shanghai: the cooling load control strategy and coefficient of performance (COP) optimization strategy. Moreover, the robustness of the proposed strategy is validated by performing a comparison with the deterministic multi-objective strategy, which assumes that the measured system data is noise-free. The simulation results suggest that the proposed strategy can be customized to satisfy different optimization requirements such as less energy consumption or a high level of indoor comfort. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a new general method for building simulation programs which is intended to be used for the modelling of complex facades. The term ‘complex facades’ is used to designate facades with venetian blinds, prismatic layers, light re-directing surfaces, etc. In all these cases, the facade properties have a complex angular dependence. In addition to this, such facades very often have non-airtight layers and/or imperfect components (e.g. non-ideal sharp edges, non-flat surfaces, …). Therefore building planners often had to neglect some of the innovative features and to use ‘work-arounds’ in order to approximate the properties of complex facades in building simulation programs. A well-defined methodology for these cases was missing. This paper presents such a general methodology. The main advantage of the new method is that it only uses measureable quantities of the transparent or translucent part of the facade as a whole. This is the main difference in comparison with state of the art modelling based on the characteristics of the individual subcomponents, which is often impossible due to non-existing heat- and/or light-transfer models within the complex facade. It is shown that the new method can significantly increase the accuracy of heating/cooling loads and room temperatures. [ABSTRACT FROM AUTHOR]

Abstract: Earth–pipe–air heat exchanger (EPAHE) systems can be used to reduce the cooling load of buildings in summer. A transient and implicit model based on computational fluid dynamics was developed to predict the thermal performance and cooling capacity of earth–air–pipe heat exchanger systems. The model was developed inside the FLUENT simulation program. The model developed is validated against experimental investigations on an experimental set-up in Ajmer (Western India). Good agreement between simulated results and experimental data is obtained. Effects of the operating parameters (i.e. the pipe material, air velocity) on the thermal performance of earth–air–pipe heat exchanger systems are studied. The 23.42m long EPAHE system discussed in this paper gives cooling in the range of 8.0–12.7°C for the flow velocities 2–5m/s. Investigations on steel and PVC pipes have shown that the performance of the EPAHE system is not significantly affected by the material of the buried pipe (pipe). Velocity of air through the pipe is found to greatly affect the performance of EPAHE system. The COP of the EPAHE system discussed in this paper varies from 1.9 to 2.9 for increase in velocity from 2.0 to 5.0m/s. [Copyright &y& Elsevier]

This paper presents a practical method to optimize in-building section of centralized Heating, Ventilation and Air-conditioning (HVAC) systems which consist of indoor air loops and chilled water loops. First, through component characteristic analysis, mathematical models associated with cooling loads and energy consumption for heat exchangers and energy consuming devices are established. By considering variation of cooling load of each end user, adaptive neuro-fuzzy inference system (ANFIS) is employed to model duct and pipe networks and obtain optimal differential pressure (DP) set points based on limited sensor information. A mix-integer nonlinear constraint optimization of system energy is formulated and solved by a modified genetic algorithm. The main feature of our paper is a systematic approach in optimizing the overall system energy consumption rather than that of individual component. A simulation study for a typical centralized HVAC system is provided to compare the proposed optimization method with traditional ones. The results show that the proposed method indeed improves the system performance significantly. [Copyright &y& Elsevier]

[Display omitted] • A whole system approach to studying a low-temperature HGSHP system. • 3D coupled TH model with realistic boundaries and dynamic heat pump COP. • Design of a HGSHP system to support a 5th generation DHC network. • Different factors on performance of the HGSHP system and ground thermal behaviour. • Ground moisture transfer, cooling process and buried depth are important. Exploitation of shallow ground and its low-grade heat potential is fundamental to designing 5th generation district heating and cooling (DHC) networks. Horizontal ground-source heat pump (HGSHP) systems are a common way to utilize shallow geothermal energy. Realistic estimation and prediction of performance of a HGSHP system and shallow ground thermal behaviour should consider the whole system including building heating and cooling load, heat pump and ground heat exchanger, and the ground. This should be accompanied by realistic atmospheric and ground conditions. In this paper, a three-dimensional coupled thermal–hydraulic model with realistic boundary conditions adopting a whole system approach is presented. Dynamic heat pump coefficient of performance (COP) that depends on seasonal variation of heating/cooling demand and ground conditions are also considered. Model validations are conducted against experimental and analytical results in literatures. The model is applied for evaluating a HGSHP system to support development of a 5th generation DHC network on a potential site in the UK. Several influencing factors, such as ground moisture transfer, building thermal load mode, buried depth of ground loops, and initial ground temperature profile are studied to assess performance efficiency of the HGSHP system and evolution of ground thermal behaviour in response to heat extraction or rejection into the ground. The results show that 5% of the monthly total heat demand of the site could be met by the designed HGSHP system, consisting of 200 U-shaped ground loops buried at the depth of 3 m and pure water as the heat carrier. Overlooking the ground moisture transfer or hyperbolizing the ground saturation would overestimate the load-carrying capacity of the HGSHP system. The HGSHP system is more efficient with a higher heat pump COP under the heating and cooling mode than under the heating-only mode. Predicted performance of the HGSHP system improves with buried depth of the ground loops. The results also show that a 1 ℃ increase in the undisturbed ground temperature could suffice up to 8% of the monthly total heat demand of the site. [ABSTRACT FROM AUTHOR]

This paper investigates the climate change impact on cooling energy consumption of office buildings in four major architectural climate zones in China. The results show that there are apparent differences in the responses of monthly or yearly cooling loads to climatic change in different climates. Dominant factors affecting monthly cooling loads change from dry bulb temperature (DBT) to wet bulb temperature (WBT) with the climate zones changing from severe cold to hot. The yearly cooling load was mainly afftectd by DBT in severe cold climate, whereas the WBT had the dominant effect in the other climates. With the continuous warming climate, the yearly cooling load did not rise significantly for any climate zone except for severe cold climate zone where cooling load had a significant increase ( p  < 0.01). More importantly, the cooling load showed a weak decrease in Guangzhou. These results suggest that different measurements for buildings cooling energy conservation should be made in different climate zones. The humidity effect should be fully considered to improve buildings energy efficiency, especially in the hot climate zones. The continuous increase of temperature in the future does not necessarily increase cooling loads of buildings in the cold, hot summer & cold winter and hot summer & warm winter climate zones, and change trendency of humidity in the future need to be considered to predict cooling energy consumption. [ABSTRACT FROM AUTHOR]

Configuring the number and size of chillers in a multiple-chiller plant properly is an efficient way to improve the plant energy efficiency. At the design stage, the optimal configuration can be achieved through matching the capacity to load as closely as possible across the full-load profile. However, in spite of the fact that current literature offers practical recommendations, a systematic method to optimize the configuration of multiple-chiller plants is lacking. Due to the lack of accurate information at the design stage and only limited knowledge of the eventual realization it is hard to predict the building’s cooling load. Moreover, there is no operational data to predict the system performance. Both explain the existence of uncertainty in the HVAC plant design process. This paper, therefore, proposes a strategy to optimize the configuration of multiple-chiller plants, which takes account of the load side uncertainty as well as the COP uncertainty and selects the optimal configuration through a life-cycle analysis. Both the load side uncertainty and the COP uncertainty are quantified using statistical distributions. To facilitate applications, the distributions of the cooling load profile of different types of buildings under different weather conditions are investigated and are classified into four categories, and the optimal configuration schemes under each type of cooling load distribution are analyzed and summarized in a tabulated form. [ABSTRACT FROM AUTHOR]

Cooling load accounts for more than 70% of the electric energy used in buildings in the Middle East. This paper presents a passive method to reduce the cooling load in buildings. The proposed concept includes adding an external solar shield enclosing an air gap between it and the outer wall of the building. This external solar shield layer would absorb the solar radiation incident on the outer walls of the building and get heated inducing a convective airflow in the gap between the layer and the building. This is an example of solar chimney in a flat geometry with a superficial resemblance to Trombe wall. Conventional Trombe wall channel heats the building external wall and creates a convective flow in-between a transparent glass layer and the heated wall. This hot flow is used either as a warm inlet air to the building in winter or help inducing airflow from inside the building for ventilation in summer. However, in the present work, the solar chimney induced airflow is purely external to the building. The proposed external solar shield starts with a glass layer from outside followed by an opaque porous metallic layer followed by an air channel before the building outer walls. The absorbing porous layer would get heated up quickly and transfer its heat to the air in the gap effectively due to its large surface area. The porous layer thickness is optimized at 15 cm while the air gap is optimized at 0.3 m. Numerical simulations based on finite volume analysis using ANSYS Fluent were carried out for a wall of 3 m height. Comparisons of the performance of buildings with its outer walls shielded by white painted, black painted and glazed walls are presented and discussed with respect to that of buildings with bared outer walls as a control. Putting glazing alone after an air gap, expectedly, increases the heat gained by 47–56% compared to 0.7 emissivity simple bared wall. Single black and white solar shielding walls reduce the heat gain by 30 and 67%, respectively. However, the proposed design of a solar chimney with a glass outer wall followed by a porous foam metal layer on its inner surface, reduced thermal gain by 67–79%. This results in electrical cooling load reduction by about 28%. Furthermore, the heated airflow induced by a solar chimney formed with a 20 × 3 m south wall can be utilized to induce water evaporation of 625–1046 kg/day allowing active evaporative cooling and/or water desalination under solar irradiation condition of Dhahran, Saudi Arabia [ABSTRACT FROM AUTHOR]

The building load forecasting with data-driven technologies guides the operation of the energy system. For new buildings or existing buildings with inadequate monitoring systems, transfer learning can be used to improve load forecasting accuracy. However, without an information integrity judgment, inappropriate migration features and incorrect transfer learning models will reduce the effectiveness of transfer learning. To bridge the gap, a data integrity judgment method is proposed in this paper to determine whether building features are absent. A max-relevance min-redundancy feature selection method with diffusion kernel density estimation (DKDE-mRMR) is established to select the key migrated features. With the transfer component analysis method used as the transfer learning model, the load data of three office buildings are taken as minimum feature set, source domain and target domain respectively for case study. The results prove that the CV-RMSE of load forecasting is over 41.0% by using insufficient information, whereas it can be reduced by 21.6% when information migration is deployed. Even if the target building after feature transfer is further missing the electricity consumption information for cooling load forecast, the CV-RMSE can still be maintained below 21.1% with good robustness. [ABSTRACT FROM AUTHOR]

This paper aims at investigating the effect of combining thermal insulation with high thermal inertia on building’s thermal performance in a hot semi-arid climate. The impacts of these passive techniques on the cooling/heating loads and the indoor comfort are analyzed by means of dynamic simulation as well as on-site monitoring of a two floor detached house located in the Green City of Benguerir in Marrakech region (Morocco). This house, mostly built using local materials, integrates many other passive and hybrid systems. The present study focuses on the hemp-gypsum, the double slab technique for thermal insulation, the double glazing and the high thermal inertia of the walls. Thermal performance of these techniques are assessed by comparison to a hypothetical reference case of the house lacking the considered techniques and built with regular classic construction materials. The results show that the investigated passive systems have a remarkable effect on the indoor air temperature, the thermal comfort and the cooling/heating loads of the building. When the three systems are integrated into the studied house, better comfort conditions are achieved compared to the reference case as its indoor air temperature minimum is up to 5 °C higher in winter while the indoor air temperature maximum is reduced by up to 9 °C in summer. In addition, the daily air temperature amplitude (peak-to-peak difference) is drastically reduced compared to the reference case. Moreover, the discomfort hours based on an acceptable thermal comfort zone of 16 °C–28 °C are reduced by 62% compared to the reference case. Furthermore, the house performs up to 81% of reduction in the annual cooling and heating load compared to the reference case. [ABSTRACT FROM AUTHOR]

This paper presents a systematic method to select an inverse blackbox model that can characterize the building-level heating and cooling load patterns parsimoniously. To this end, hourly heating, cooling, and electrical load data were gathered from five office buildings. In addition, concurrent weather data for temperature, solar irradiance, wind speed, and humidity were collected. Using the recent history of weather and electrical load data from the past three hours, 18 different forms of model at varying number of inputs and parameters were formulated for each of the five buildings. After assessing the models’ performance through a cross-validation and a residual analysis, one of the models was selected. The selected model was the one with a one-layer artificial neural network, six inputs, and a one-hour input history. Then, through illustrative examples, different use-cases in which inverse blackbox models can support the operational decision making process are discussed. [ABSTRACT FROM AUTHOR]

• Selected the outdoor design conditions based on simultaneous occurrence probability. • Analyzed the dependence relation between three weather elements using vine copula. • Modeled the multivariate joint distributions of three weather elements. • The indoor thermal risk levels of outdoor design conditions were evaluated. In building design, the commonly used outdoor weather conditions recommended by ASHRAE are generated by the annual cumulative frequency of occurrence method. This may result in air-conditioning equipment capacity being overestimated. This is because the air temperature, humidity and solar radiation that have the greatest impact on indoor peak cooling loads are selected independently. It means that the probability of these three weather parameters occurring at the same time is extremely small. This paper proposes an approach for selecting the design weather data on the basis of the exact joint distribution with specific simultaneous occurrence probabilities of these three weather parameters. For this purpose, a novel statistical method vine copula is applied to construct the theoretical multivariate joint distributions of these three weather parameters for 24-hour data and combined the design parameters of each hour with a specific simultaneous occurrence probability into a design day. The hourly meteorological records over 10 years in Tokyo are used to generate the outdoor design conditions. The results indicate that the vine copula method can create an effective theoretical model and all the design parameters selected by the simultaneous occurrence probability are significantly below that of the same conventional exceeding probability. For simultaneous occurrence probabilities of 0.4 %, 1 % and 2 %, the corresponding indoor thermal risk levels are 0.46 %, 0.52 % and 0.84 %, respectively. Therefore, the proposed method can be used as a reference for designers to determine a more reasonable capacity of air-conditioning systems. [ABSTRACT FROM AUTHOR]

Accurate cooling load prediction has continually attracted interest due to its important role in building energy saving. Much attention to existing methods has been paid to the temporal information of the features that affect the load prediction. However, facing the time-varying interactions between various types of features, existing approaches have limited ability to extract the implicit information of the complex associations among them. This limitation leads to underutilization of features, especially those that indirectly affect the load prediction through such interactions, and further constrain prediction accuracy. To this end, this paper proposed a graph neural network-based cooling load prediction method to integrate the associations and temporal information of the features. Using the weighted mask mechanism, a knowledge embedded dynamic association graph is constructed to handle the time-varying feature associations rationally. By introducing empirical knowledge and adjusting the graph structure automatically according to the real-time data, the proposed method can provide accurate cooling load prediction. Comprehensive experiments are carried out on the onsite data, and the experimental results show the practicability and effectiveness of the proposed method for cooling load prediction. [ABSTRACT FROM AUTHOR]

A promising way to significantly reduce the total cost of ownership (TCO) of a data center is to minimize the cooling system energy consumption. To come up with the most energy efficient cooling system design, detailed analysis of various configurations based on local climate data is crucial. In this paper, an enterprise data center of 180 ton (TR) cooling load is modeled using a transient energy analysis software TRNSYS with the operating environment in the data center maintained as per the guidelines of ASHRAE TC 9.9. Promising cooling strategies including water side economizer and indirect evaporative cooler (IEC) are modeled and analyzed for the 17 worldwide climate zones defined by ASHRAE 90.1. Energy savings potential of these strategies is presented. Amount of water consumed and the number of hours the strategy is active in a year is also estimated. Results indicate significant energy saving potential of water side economizer and IEC in most of the zones. As compared to air cooled chiller systems, more than 30% energy savings can be achieved in climate zones 3–7 by using IEC. Also, more than 25% energy savings can be achieved by using water side economizer in climate zone 4 and 5. [ABSTRACT FROM AUTHOR]

Due to the inherent complexity of multi-dimensional solutions for transient conduction in a multilayered building element, most heat balance-based algorithms assume that conduction occurs only in one dimension—through the building element between the inside and outside environments. Usually, since the length and width of the elements are much greater than the thickness of the element, this is a valid assumption. However, there are some cases such as hydronic radiant slabs where this assumption may not lead to an accurate estimate of the energy consumption of the system. In a hydronic radiant slab, water tubes are embedded in a slab, and hot or chilled water is circulated to meet any heating or cooling loads. The energy consumption of such a system will depend highly on the temperature of the water being sent to the slab (i.e., produced by the boiler or chiller). The temperature of the water that must be sent to the slab will depend on the spacing of the tubes, the multilayer construction of the building element, and the boundary conditions to which the construction is exposed. Thus, to accurately predict the energy consumption of such a system, a transient two-dimensional heat conduction model must be integrated with a heat balance-based simulation. This paper demonstrates the potential loss of accuracy associated with the one-dimensional radiant system models by providing a comparison of one- and two-dimensional solutions using EnergyPlus. [ABSTRACT FROM AUTHOR]

• A new design-day hourly variation coefficient generation method. • Three-parameter coupled coefficient joint matrix and K-Means clustering are used to build representative coincident coefficients. • Evaluation of reasonableness based on different load scenarios. • Suitable for different climate zones and typical buildings. The design day is the basis for calculating the cooling load, and a reasonable design cooling load is essential for balancing economics and reliability. The coincidence among weather parameters of design day is a key means to ensure the accuracy, while the coincident design-day hourly variation coefficients are less considered in existing studies. For this reason, this paper proposes a method to establish the coincident hourly variation coefficient for design days. The method first extracts the normalized hourly variation coefficients from 30 years of historical weather data. Based on this, a three-parameter coupled coefficient joint matrix is established and K-Means clustering is used to select representative coincident coefficients. Finally, a "typical line" method based on the weighting idea is proposed, and the final coincident design-day hourly variation coefficients are selected from the actual weather. To verify the rationality of the new method, different load scenarios were constructed with three design days and five typical buildings, and the rationality index was established by the ratio of indoor to outdoor risk levels. The results show that the rationality of the new method reaches 0.81, while the original method (Chinese and ASHRAE standard) is only 0.03 and 0.25 in Tianjin; further study shows that the new method is applicable in different climate zones and between different typical buildings, which provides a basis for the improvement of the existing design day generation method. [ABSTRACT FROM AUTHOR]

This paper assesses the effectiveness of Phase Change Materials (PCMs) for the improvement of the thermal performance and the thermal comfort of a residential building in Melbourne. The incorporation of PCMs in buildings with their significant heat storage capacity can delay the heat transfer and reduce the cooling and heating loads. Numerical simulation is a useful tool for comprehensive assessments and optimization of PCM applications in buildings. Thus an available TRNSYS component, PCM Wall: Type1270, was implemented with Type56 (Multi zone component). PCM Wall TRNSYS component has been validated with some experimental data published in the open literature. The validated model was then utilised to simulate the thermal performance of a residential building which has a PCM roof layer. The building is a typical single-storey, three bed room residential building in Melbourne. It was found that the PCM roof layer can reduce the cooling and heating loads whilst providing better thermal comfort for occupants with reduced indoor temperature fluctuations. [ABSTRACT FROM AUTHOR]

In building energy simulation, researchers commonly use weather data obtained from the software database or (meteorological) MET station. However, this method leads to disparity of urban-rural temperature condition, hence the input weather data do not characterize the on-site condition. This paper highlights the importance of generating and implementing the urbanized weather data, primarily on the ambient temperature. A parametric approach was used to create hundreds urban layouts of the hypothetical office block plan. An annual temperature profile was generated for each scenario using the Screening Tool for Estate Environment Evaluation (STEVE) tool. It was found that the local average temperature ( T avg ) and peak afternoon period ( T max ) from these scenarios could differ 1–2 °C and 1.2–3.5 °C respectively as compared to the MET station data. Using Integrated Environmental Solutions (IES) to simulate the thermal load performance, this temperature difference can lead to an approximately 8% difference in the predicted cooling load, 20% in external conduction gain, and 17% in fresh air intake gain. These numbers can be deemed substantial in simulating building performance, especially cooling load due to changes in the external condition. Furthermore, the comparison proves to be significant in term of enhancing the simulation result and representing the studied area. [ABSTRACT FROM AUTHOR]

In the subtropics, air conditioning (A/C) for a bedroom in residential buildings commonly serves to provide an appropriate indoor thermal environment at nighttime for sleeping. During daytime when A/C is not provided, a large amount of thermal energy can be stored in the thermal mass in its envelope components, in particularly its external walls. The energy stored would be gradually released, contributing significantly to the total space cooling load in the bedroom at nighttime. Therefore, this paper reports a simulation study on the impacts of daytime external envelope heat gain/storage on the nighttime cooling load in a hypothetic west-facing bedroom and the related mitigation measures in the subtropical Hong Kong. The study results showed that, among all envelope components, the west-facing external wall contributed most significantly to the total nighttime cooling load, because of its direct exposure to solar radiation, thus the heat gain and storage in its thermal mass. In addition, adding an air gap in the west-facing external wall can reduce remarkably the hourly total nighttime cooling load for the first hour (21:00–22:00) and the total cooling load for the 10-h A/C period. However, while varying the width of either the air gap or inside/outside concrete layers would not reduce cooling load further, ventilating mechanically the air gap can further increase the cooling load reduction and improve the flatness of load variation profile at the expense of consuming additional electrical energy. Finally, adhering aluminum foil to both sides of the air gap can further remarkably reduce nighttime cooling load, and improve the flatness of the load variation profile. [ABSTRACT FROM AUTHOR]

The use of heat dissipation on buildings by means of night radiative and convective processes is an effective technical approach to meet user comfort requirements while reducing the conventional energy consumption employed in refrigeration processes. The paper describes the design and development of three different night cooling systems being installed in the Solar Platform of Almeria for experimental purpose. The three systems used low cost, easy to install panels resulting in significant reduction of the cooling load. A model of the radioconvective systems was developed taking into account the main physical and mechanical phenomena occurring in the process. The model allowed evaluating the performance of the system, and it was further compared with the experimental test results to analyze its validity. [ABSTRACT FROM AUTHOR]

The cooling load prediction of heating, ventilating and air-conditioning (HVAC) systems in office buildings is fundamental work for optimizing the operation of HVAC systems. In this paper, an improved multivariable linear regression model is proposed to predict the daily mean cooling load of office buildings in which three main measures, including the principal component analysis (PCA) of meteorological factors, cumulative effect of high temperature (CEHT) and dynamic two-step correction, are used to improve prediction accuracy. The site measured cooling load of two office buildings in Tianjin is used to validate the model and evaluate the prediction accuracy. Meanwhile, four contrast models with one or two of the three measures are also built. A comparison among the models proves that a combination of the three measures could effectively improve the prediction accuracy. The predicted load of the proposed model has acceptable agreement with actual load, where the mean absolute relative error is less than 8%. [ABSTRACT FROM AUTHOR]

• Addressed a real-world challenge for load-shifting multiple ON/OFF staged units. • Presented a unique MPC that shifts load while eliminating undesired peak. • Presented comprehensive 2-month experimental MPC demonstration results. • Achieved significant load shifting with over 30% demand savings. • Discussed experimental charging and discharging processes of building thermal mass. Small and medium-sized commercial buildings (SMCB) are significant demand response resources, and it is important to develop grid-responsive control algorithms that exploit those resources and create financial benefits for building owners and HVAC service providers. Furthermore, unlike large-sized commercial buildings, there is an opportunity to have universally applicable control solutions for many SMCBs since those buildings have a consistent HVAC system configuration: SMCBs are commonly served by multiple-staged air conditioning units controlled by their own thermostats. Despite the demand response potential and scalability, however, very few control solutions are available for SMCBs. Typical model predictive control (MPC) and heuristic control approaches for cooling load shifting that lower thermostat setpoints before an electric price jump are suitable mainly for large-sized commercial buildings where a continuous capacity modulation is possible, e.g., via dampers in variable air volume terminal units. However, those approaches can cause undesired, high peaks for SMCBs due to the nature of ON/OFF unit staging and narrow thermostat deadbands. This could discourage the use of advanced grid-responsive controls for SMCBs due to the concern of high demand charges, and has to be resolved. This paper presents a MPC solution that overcomes this challenge. It has a hierarchical MPC structure where an upper level MPC is responsible for electrical load shifting in response to an electric price signal while a lower level MPC is responsible for coordinating compressor stages to eliminate unnecessary peaks and follows the setpoints determined by the upper level MPC. Two one-month, comprehensive laboratory tests have been carried out to demonstrate load shifting and cost savings for the algorithm. Interesting trade-offs between energy efficiency and load flexibility were observed and are discussed, and lessons learned for applying MPCs for SMCBs are also presented. [ABSTRACT FROM AUTHOR]

Heating and cooling loads are the major reasons for energy use in buildings. Buildings are usually subject to schedules and set-points which are not optimized in response to the dynamic weather conditions, internal loads, and occupancy patterns. The thermal network model has been widely applied for real-time building load estimation, which is crucial for optimizing the operation of the HVAC system. However, there has been limited exploration of the capabilities of the thermal network model due to constraints imposed by the solution method adopted. In this paper, the exponential matrix method was adopted to simplify the state space equations and solve the thermal network model analytically. This enhances the applications of a simplified thermal network model for investigation of multiple scenarios of HVAC system operations and equipment sizing, and for more accurate estimation of heating and cooling loads. This study also proves that the analytical solution method is asymptotically stable regardless of time step. A typical office was used as a case study and the predicted building loads are compared with measured data and numerical results from EnergyPlus. For the case study, the model demonstrated better accuracy and is seen to be robust for thermal load estimation for cooling season. [ABSTRACT FROM AUTHOR]

In this paper, the cooling load of a lecture hall in a hot, humid climate requiring sustainable energy consumption is investigated experimentally and numerically. The polyethylene aluminium single bubble (PASB) is used as an insulating material placed on the external wall for the lecture hall with dimensions (15 m × 12 m × 6.6 m) at University Putra Malaysia (UPM), Selangor, Malaysia. The measurements of air temperature, air velocity, relative humidity and the number of students using the lecture hall for the whole month of April 2012 were used as input parameters for the three-dimensional computation of fluid dynamics (CFD). The results show that the potential temperature reduction inside the lecture hall was around 3 °C when using the (PASB) as insulating material, leading to a potential saving of 500 USD per month. [ABSTRACT FROM AUTHOR]

Underfloor air distribution (UFAD) system, which is a relative new method of providing conditioned air and ventilation to the commercial buildings and data centers, is focused by researchers and designers for its potential advantages of better indoor thermal comfort (ITC) and indoor air quality (IAQ), layout flexibility, reduced life cycle costs, and energy saving. However, the application of UFAD system are still obstructed by the gap in the fundamental understanding of some new elements, such as the cooling load calculation, control of the thermal stratification, and evaluation of energy consumption. This paper reviews the current situation of UFAD technology, and the existing problems in recent researches are summarized. To improve the performance of UFAD system, several possible directions for the further studies are suggested. [ABSTRACT FROM AUTHOR]

Night ventilation may improve the indoor air temperature and reduce the cooling load. However, the effects of different night ventilation rates on the indoor air relative humidity, wall inner surface temperature, inner surface moisture flux, sensible heat load, latent heat load and total load are rarely studied in terms of the inner surface moisture transfer. The present paper aims to investigate the effect of different night ventilation rates on the above items in a hot-humid climate in China. The results show the following: when the moisture absorption–desorption process at the wall inner surface is not considered, the air-conditioning load of the night ventilation at 15ACH is approximately 5% lower than that at 5ACH. When the moisture absorption–desorption process is considered, the night ventilation rate has a smaller impact on the indoor air temperature and has a larger impact on the indoor air relative humidity during the night. The night ventilation rate negatively correlates with the sensible heat load and positively correlates with the latent heat load due to the increased amount of moisture that is absorbed or desorbed at the interior wall surface. The total air-conditioning load of Case B.1, which featured a 15ACH night ventilation rate, was approximately 8% higher than that of Case A.1, which featured a 5ACH night ventilation rate. [ABSTRACT FROM AUTHOR]

• Two-stage clustering was developed to recognize multiple energy load patterns. • Proposed approach can depict the load fluctuations at different time scales. • A series of exploratory studies on distribution of load patterns were launched. • Uncertainty of multiple energy load patterns was quantified by entropy. • Multi-energy consumption data from 122 buildings were used for method validation. The energy consumption data of buildings contain plenty of knowledge that can be used to enhance demand side management (DSM). However, existing research rarely conducts comprehensive analysis on the multiple energy characteristics for the same building group simultaneously. Additionally, DSM requires the load side to meet the flexibility demands at multiple time scales. To address these problems, the paper presents a strategy with two-stage clustering to recognize multiple energy load patterns of higher education buildings at different time scales. In this work, the low frequency component (LFC) and high frequency component (HFC) are obtained from daily load curve by wavelet decomposition, which are clustered by two-stage clustering to construct typical patterns of load at different time scales. Based on these patterns, a series of exploratory studies on their characteristics are launched. The influence characteristic of factors on multiple energy load patterns are revealed. The results show that different functional buildings have obvious differences in the distribution of their electrical LFC patterns on working days and nonworking days, and the shapes of the dominant patterns of power load and cooling load are more consistent with the change of the ambient temperature throughout the day. In addition, the uncertainty of the load pattern is quantified by entropy, which is used to reflect the order degree of the load pattern change over time for each building. The results indicate that probability of a pattern remaining unchanged is higher than transitioning to other patterns, and the uncertainty of HFC pattern for power load is generally higher than that of LFC. The results obtained from this study could be potentially used to enhance DSM. [ABSTRACT FROM AUTHOR]

• Metal hydrides show potential for year-round energy storage with residential HVAC. • Hydride storage can shift both heating and cooling loads away from on-peak hours. • Using hydride storage results in higher cost savings in winter than in summer. • Hydride storage has a long payback period even for favorable TOU rates. • Long payback periods are due to high initial cost and hydrogen compression costs. Thermal energy storage (TES), such as ice storage, is often used with air conditioning systems to shift cooling loads away from peak hours, reducing operating costs if time-of-use electricity rates are available. Phase change materials (PCMs) have been investigated for energy storage with heating systems; however, a single PCM cannot be used for storage with both cooling and heating since PCMs have fixed phase-change temperatures. Thermochemical energy storage with metal hydrides can be used for this purpose since the reaction used to store energy depends on both temperature and pressure, allowing for variable-temperature storage. In this paper, we propose a novel TES system design using metal hydrides coupled to a heat pump for residential heating and cooling and present a system model. The model is used to analyze system performance and predict cost savings for year-round use in a residential building. This system is shown to be capable of shifting heating and cooling loads, but only achieves cost savings of $13/year at existing utility rates. The payback period of the system exceeds 35 years across a range of rates more favorable to energy storage, primarily due to the high cost of metal hydrides. Potential improvements to the system are discussed. [ABSTRACT FROM AUTHOR]

The inefficient dispatch of multiple-chiller systems results in drastic energy consumption, which should be investigated by optimal allocating of cooling loads between chillers in a building. Due to the nonlinearity of the dispatching problem, heuristic algorithms are more prevalent in this field. In this paper, a hybrid exchange market and genetic algorithm (EMGA) is established to solve the optimal chiller loading (OCL) problem. The decision variables of the problem are the partial load ratio (PLR) of chillers determining the power consumption of each chiller unit, and the objective of the OCL problem is to minimize the total power consumption of the multiple-chiller system. The effectiveness of the proposed algorithm is verified by a fair comparison of the results with similar attributes in three identical cases. It is shown that the proposed EMGA outperforms comparable algorithms in terms of convergence speed and finding optimal solutions. Moreover, the problem is modeled as mixed-integer non-linear programming (MINLP) in the general algebraic modeling system (GAMS) to solve mathematically using different solvers. It was shown that the mathematical approaches adopted by GAMS have a high probability of trapping in local optima when the decision variables are increasing, whiles the optimum solutions by the proposed EMGA are superior. [ABSTRACT FROM AUTHOR]

Buildings account for almost 40% of global energy consumption. Due to the high energy consumption of chilled water plants, various studies have optimized chiller loading in plants with multiple chillers for energy conservation. However, few studies have optimized dual-temperature chiller plants, even though better energy efficiency could be achieved than that of typical single-temperature chiller plants. This paper proposes two optimal control strategies for dual-temperature chilled water plants, strategy B and strategy C. Strategy B optimizes the cooling load distribution of the chillers in each group by adjusting the cooling load ratio of each chiller. Under this strategy, the energy consumption of the chiller plant for the entire cooling season was reduced by 10.1%. Meanwhile, strategy C optimizes the cooling load distribution among chillers in the same chiller group and between two chiller groups, by simultaneously adjusting the temperature setpoint of the air leaving the primary cooling coils and the partial load ratio of each chiller. By considering both the impact of the chilled water loop and the air handling process, strategy C achieved greater energy saving (16.4%) for the entire cooling season. In hot summer months, the energy savings arise mainly from optimization of the cooling load distribution among chillers in each chiller group, as this optimization accounts for 63–68% of the total savings. In moderate months, optimizing the cooling load distribution among chillers in the same group and optimizing the distribution between two chiller groups account for nearly the same proportion of the total energy savings. [ABSTRACT FROM AUTHOR]

[Display omitted] • Summertime overheating prevalent in both care homes, regardless of metric used. • Newer care home had less extreme but higher mean temperatures than older care home. • Bedrooms overheated more at night-time due to limited ventilation through windows. • Thermal simulation showed night-time ventilation to be the most effective strategy. • Combination of passive solutions and air-conditioning necessary as climate warms. Summertime overheating in care settings has been identified as a key risk and research priority for the health and social care system. This paper examines the current and future risk of summertime overheating in two London-based care homes occupying modern and older buildings. Continuous monitoring of outdoor and indoor temperature in bedrooms, communal areas and offices in summer 2019 helped to establish the prevalence and intensity of overheating. Dynamic thermal simulation (EnergyPlusV8.9) of the two care settings assessed the potential for avoiding active cooling in future climate using passive solutions. In both care settings, indoor temperatures were observed to exceed 30 °C during daytime hours, significantly higher than the recommended 26 °C threshold of Public Health England. Although severity of overheating was lower in the older building, overheating was found to be prevalent and prolonged across both care settings with bedroom temperatures higher than lounges especially at night. Thermal simulation analysis showed that, with regards to temperature reduction and cooling load, nighttime ventilation was the single most effective passive solution for both buildings for the current climate, while a combination of night ventilation, external shading and high-albedo external walls was the most effective package solution. By the 2080s, air-conditioning was the most effective solution for reducing temperature, but also had the highest cooling load, highlighting the importance of balancing passive with active measures to improve thermal comfort and reduce cooling loads. [ABSTRACT FROM AUTHOR]

Abstract: The building wind pressure coefficient (C p) is an important quantity which is used in many fields of building engineering including heating and cooling load calculations, ventilation design, and structural design. C p is a dimensionless quantity that represents the proportionality between the wind velocity and the pressure generated on the surface of the building. Values for C p can be obtained from full-scale building tests, wind tunnel tests, or, more commonly, from parametric equations derived from tests. The purpose of this paper is to analyze a set of wind tunnel tests and to present a new set of surface-averaged wind pressure coefficient values for low-rise buildings and a new parametric equation determined from a curve fit to the surface-averaged data. The resulting equations are compared to another popular low-rise parametric equation and another popular wind pressure coefficient database. The new parametric equation is found to fit both databases better than the older parametric equation. [Copyright &y& Elsevier]

Abstract: Heat flow through the roof contributes in the total heat gain of a building. The contribution becomes more severe for single story houses covered by metal sheet roofing. This paper presents a new design for metal sheet roofing structure in order to improve its total thermal resistance. Its main concept is to utilize phase change material properties to firstly absorb the downward heat flow made by incident solar radiation to the room and then release it back to the environment by means of the naturally favored external convection especially during the nocturnal cycle. Experimental and numerical analyses have been conducted to compare thermal performance of the new design and the available normal roofing with the same materials and color. A general mathematic equation system, which can be used for further evaluation and prediction of the new design with varied parameters, has been derived for the thermal process through the roof. The results show that the new design can effectively reduce the downward thermal flow through the roof into the house. As a consequence, the cooling load of the house can be lower and more electricity for cooling attempt can be saved. [Copyright &y& Elsevier]

Abstract: Complaints on thermal comfort and confusions on cooling load calculation are still two main issues for the design of stratified air distribution (STRAD) system. This paper investigated these two issues for STRAD systems applied in a large space lecture theatre. The simulation results revealed that best satisfied thermal environment was obtained when supplied air from floor level, terrace and desk-edge mounted grilles simultaneously. A new method for the calculation of the occupied zone cooling load, which is based on CFD (computational fluid dynamics) simulation was proposed and tried. The effective cooling load factor concept was further clarified, which can be conveniently used to calculate the occupied zone cooling load and then determine the supply airflow rate. The concepts of occupied zone cooling load reduction and the associated but different cooling coil load reduction were differentiated, and it was illustrated that cooling coil load reduction up to 16.5% of the space cooling load can be achieved with split location of return and exhaust grilles. These simulation works provide a good basis for full-scale experimental validation of the key energy saving features of a well-designed stratified air distribution system. [Copyright &y& Elsevier]

Abstract: This paper presents an investigation into the use of occupancy space electrical power demand to mimic occupants’ activities in building cooling load prediction by intelligent approach. The occupancy space electrical power demand is obtained from an intelligent networked building power monitoring system. It works as a prototype advanced metering infrastructure – a key feature in smart grid technology. The artificial neural network model adopted is the Levenberg–Marquardt algorithm. The input parameters include the usual external climatic data, hour-type/day-type and pretreated air unit operation schedule, and the occupancy space electrical power demand. The output is the electrical power demand of the building cooling system. Simulation studies are conducted for a university building in Hong Kong. The 2010 and 2011 yearly data is used to conduct simulations. The performance indices used in evaluating the prediction performance are the coefficient of correlation (R), coefficient of variation (CV) and mean absolute percentage error (MAPE). It is demonstrated that with the use of occupancy space electrical power demand as one of the model input parameters, the prediction accuracy of the building cooling load model can be improved. In summer season, the best MAPE and CV is 4.494% and 5.808% respectively for hourly prediction, and is 1.935% and 2.345% respectively for daily prediction. On daily peak cooling load prediction in summer season, the best MAPE and CV is 2.313% and 2.862% respectively. It is found that the variability in the prediction is modest in summer season. [Copyright &y& Elsevier]

Abstract: In Hong Kong, conventional air-conditioning (AC) systems cause a high energy consumption and poor indoor thermal comfort. This paper investigated the AC load profiles of three typical local commercial buildings, i.e. office, hotel and retail, and examined the energy consumption of liquid desiccant cooling system (LDCS) for these buildings. Simulation results showed that the cooling load contributed to more than 98% of total annual load, with high latent load proportion from March to September. The high reheating ratio, i.e. from 60% to 90%, is found to be the main reason for the poor thermal comfort. Therefore, the LDCS which could handle sensible and latent loads separately was proposed. However, the electricity-driven LDCS was found not suitable for local commercial buildings. significant energy saving could be achieved by the solar-assisted LDCS system with thermal storage, when the installation area for solar collectors is larger than the minimum required one, i.e. 9.7m2/(kWm−2) for office and 12.7m2/(kWm−2) for hotel buildings. Energy saving of different system types with different installation areas was also studied. The solar-assisted LDCS shows more energy saving in winter but less in summer. Results and conclusions can be useful for researchers and local engineers. [Copyright &y& Elsevier]

Abstract: Window design, especially glazing choices, is a critical factor for determining the effectiveness of passive solar design. Windows are like a knife has two side; one is useful and the other is harmful. In this paper the effects of windows’U-value, window orientation and windows size on annual heating and cooling energy demand is studied considering the both energy and investment costs. The study has been performed for three different climate zones; Amman, Aqaba and Berlin. Four types of windows have been studied; single glazed, double glazed L, double glazed H and triple glazed. The results show that heating load is highly sensitive to windows size and type as compared with cooling load. Also, it is shown that with a well-optimized glazed window energy saving can be reached up to 21%, 20% and 24% for Amman, Aqaba and Berlin, respectively. [Copyright &y& Elsevier]

Abstract: The cooling degree days concept is a tool to estimate and analyze weather related energy consumption in buildings, i.e. the cooling system electric energy consumption. The main problem with applying this method is that it disregards latent cooling loads. This paper deals with an approach for monitoring electric energy consumption due to cooling in buildings based on cooling degree days, which allows an estimation of latent loads. In addition to applying methods for determining base temperature to base humidity, a new technique is introduced, which is based on a significance test of the enthalpy latent days partial regression coefficient. Analogous to the performance line concept, the influence of latent loads can be presented in the form of a “performance surface” graph. The performance surface is a plot of electric energy consumption as a function of cooling degree days and latent enthalpy days. The above methods are tested on data sets consisting of electric energy consumption data obtained from two buildings and hourly meteorological data. [Copyright &y& Elsevier]

Abstract: This study employs differential evolution algorithm to solve the optimal chiller loading problem for reducing energy consumption. To testify the performance of the proposed method, the paper adopts two case studies to compare the results of the developed optimal model with those of the Lagrangian method, genetic algorithm and particle swarm algorithm. The result shows that the proposed differential evolution algorithm can find the optimal solution as the particle swarm algorithm can, but obtain better average solutions. Moreover, it outperforms the genetic algorithm in finding optimal solution and also overcomes the divergence problem caused by the Lagrangian method occurring at low demands. [ABSTRACT FROM AUTHOR]