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Occupancy is one of the main factors affecting building energy consumption. The occupancy data, which refer to the occupancy number in this paper, has been widely used in the building simulation field. However, due to the stochastic nature of occupant behavior, it is hard to predict and measure how many people stay in a given building. The rapid development of mobile Internet technology provides an efficient and convenient option for occupancy detection. This paper proposes a concept of typical occupancy data (TOD), which are extracted from real-time occupancy data collected by mobile devices. K -means algorithm is employed to generate the TOD data through cluster analysis. An energy performance model of an office building is used as a case study to demonstrate the effectiveness of the TOD data. [ABSTRACT FROM AUTHOR]

Clustering algorithms have been successfully applied in analyzing building energy consumption data. It has proven to be an effective technique to identify representative energy consumption patterns as well as being a pre-processing step for other techniques. In this paper, we propose a clustering method based on k-shape algorithm, which is a relatively novel method to identify shape patterns in time-series data. In the experiment, clustering is performed for each individual building according to its hourly consumption. The novelty of this paper is that a new k-shape algorithm is applied to detect building-energy usage patterns at different levels, and the clustering result is further utilized to improve the accuracy of forecasting models. Ten institutional buildings covering three different typologies are used as case studies and a set of hourly and weekly energy consumption data is further analyzed in this paper. The experimental results reveal that this proposed method can detect building energy usage patterns in different time granularity effectively and also proves that the forecasting accuracy of SVR model is significantly improved by utilizing the results of the proposed clustering method. [ABSTRACT FROM AUTHOR]

• FDD tools are increasingly adopted in commercial buildings. • Using FDD tools to correct hunting faults can mitigate issues cost-effectively. • We developed and implemented automated control hunting correction in FDD tools. • The algorithm was tested in nine VAV boxes and successfully eliminated hunting faults. • We discussed challenges and future research areas for scaling the technology up. Control hunting due to improper proportional–integral–derivative (PID) parameters in the building automation system (BAS) is one of the most common faults identified in commercial buildings. It can cause suboptimal performance and early failure of heating, ventilation, and air conditioning (HVAC) equipment. Commercial fault detection and diagnostics (FDD) software represents one of the fastest growing market segments in smart building technologies in the United States. Implementation of PID retuning procedures as an auto-correction algorithm and integration into FDD software has the potential to mitigate control hunting across a heterogeneous portfolio of buildings with different BAS in a scalable way. This paper presents the development, implementation, and field testing of an automated control hunting fault correction algorithm based on lambda tuning open-loop rules. The algorithm was developed in a commercial FDD software and successfully tested among nine variable air volume boxes in an office building in the United States. The paper shows the feasibility of using FDD tools to automatically correct control hunting faults, discusses scalability considerations, and proposes a path forward for the HVAC industry and academia to further improve this technology. [ABSTRACT FROM AUTHOR]

Harvesting daylight for improving building energy performance has become an important issue in recent years. Most energy simulation tools use a Daylight Factor (DF) based algorithm to capture the complexity of daylight behaviors. However, DF has a limitation for fully accounting realistic daylight conditions, and several modifications or integrations with other supporting tools were developed to compensate for this limitation. This paper uses the Kriging method to predict daylight conditions for a whole year, and couples this method with energy simulation tools. Kriging is a group of geo-statistical techniques used to interpolate the value of a random field at an unobserved location from discrete observed values at nearby locations. In previous research, the Kriging method showed promising outcomes; that result was significantly better than current daylight modeling in energy simulation tool, and very close to the results from advanced physics-based daylight simulation tool. However, Kriging also comes with certain limitations that require further investigation. One major hurdle was Kriging’s long computational time compared to current daylight models in energy simulation tools. For this reason, this paper aims to find possibilities to reduce computational time to realistic levels that can be applicable in practice. Furthermore, it proposes a method to integrate Kriging-based daylight model with an energy simulation tool that can plot hourly indoor light distribution. This proposal obtains more realistic results than current energy simulation daylight models, while reducing computational expense. [ABSTRACT FROM AUTHOR]

Kriging is a group of geo-statistical techniques used to interpolate the value of a random field at an unobserved location from discrete observed values at nearby locations. Kriging has been very popular in oceanic research used for predicting the variance of geographical conditions. However, it has been difficult to find a similar application in the built environment. This paper aims to uncover possibilities of using Kriging for built environment studies, specifically focusing on indoor daylight modeling. Currently, most energy simulation tools use algorithms that are based on Daylight Factor (DF) [1] to capture the complexity of daylight behavior. However, DF has limitations in its ability to fully account for realistic daylight conditions. Thus, efficiently integrating the light domain with thermal calculations remains a major hurdle for building energy simulation tools [2] . For this reason, the paper aims to find possibilities of using Kriging for daylight modeling. Kriging will use sample points to predict illuminance values of nearby locations. Kriging will considerably reduce the number of points needed to map indoor light intensity profiles. Furthermore, Kriging will be able to provide a more seamless daylight model for energy simulation tools. This proposed method can produce light distribution, such as illuminance (lux), while obtaining more realistic results than DF and at the same time reduce computational expense through statistical methods. The paper first investigates accuracy to determine whether Kriging modeling can interpolate the profile of illuminance from a select sample of data points. To validate the daylight model used in this paper, real indoor illuminance levels were measured for different days and compared with the daylight simulation model. Further, this paper tests Kriging predictions and compares them with both advanced physics-based light simulation and energy simulation to determine possibilities and limitations of the proposed method. In comparison with RADIANCE, this paper found that the proposed Kriging model was able to predict better than the energy simulation daylight model. The difference between Kriging and RADIANCE was 25.52 lux. The Kriging model showed great promise since it was able to cut the full RADIANCE simulation time in half and proved to be much closer to RADIANCE predictions than other methods. [ABSTRACT FROM AUTHOR]

Abstract: Voltage rise is one of the main factors which limits the capacity of Low Voltage (LV) network to accommodate more Renewable Energy (RE) sources. This paper proposes a robust and effective approach to coordinate customers’ resources and manage voltage rise in residential LV networks. PV is considered as the customer RE source. The suggested coordination approach in this paper includes both localized control strategy, based on local measurement, and distributed control strategy based on consensus algorithm. This approach can completely avoid maximum permissible voltage limit violation. A typical residential LV network is used as the case study where the simulated results are shown to verify the effectiveness of the proposed approach. [Copyright &y& Elsevier]

Abstract: Significant energy savings can be achieved by optimizing chiller operation and design in heating, ventilation and cooling (HVAC) systems. In terms of optimization, various metaheuristics have been proposed to the optimal chiller loading problem. New metaheuristics are also emerging recently, between them the firefly algorithm. Firefly algorithm is a nature inspired algorithm based on the idealized behavior of the flash pattern and characteristics of fireflies. This study proposes a new improved firefly algorithm (IFA) based on Gaussian distribution function to the optimal chiller loading design. To testify the performance of the proposed method, the paper adopts two case studies comparing the results of the developed model using IFA with those of traditional firefly algorithm and other optimization methods in literature. In this paper, the optimization problem is minimize energy consumption of multi-chiller systems, where the objective function is energy consumption and the optimum parameter is the partial loading ratio of each chiller. The results of both case studies show that the proposed IFA outperform several optimization methods of the literature in terms of minimum energy consumption solution of the optimal chiller loading problem. [Copyright &y& Elsevier]

Abstract: The paper presents a PI tuning strategy for air handler discharge air temperature control in building comfort application. It recommends using a 5min settling time with no overshoot as the design criterion to select PI gain values, and recommends that a PI controller should be retuned when the settling time is larger than 20min. This tuning strategy avoids unnecessary tuning while providing acceptable control performance for building comfort control. The paper further concludes that PI tuning can be done by adjusting proportional gain only and keeping the reset time constant for air handler discharge air temperature control. The proportional gain can be calculated based on the system sum of time constant and delay, and system static gain in typical applications. Inclusion of the time constant in the equation is recommended for uncommon applications where the delay is large. Regression equations are given based on simulation results for both typical applications and uncommon applications. The PI tuning strategy proposed in the paper can be reliably used in PI auto tuning since it uses less model information and adjusts less gain values than existing tuning algorithms. [Copyright &y& Elsevier]

Abstract The refrigerant leakage would occur in a multi-split variable refrigerant flow (VRF) system after years of operation, thus contributing to the inefficient operation and even increased energy consumption. Diagnosing the refrigerant charge amount (RCA) malfunction in time is of great necessity to ensure the normal operation of VRF system and avoid superfluous energy waste. This paper proposes an efficient online weighted k-Nearest-Neighbor model (wk NN) strategy to diagnose the RCA malfunction of the VRF system, which is based on the coupled feature selection method. The minimal-redundancy-maximal-relevance (mRMR) algorithm is first applied to derive the subset of features that have the maximum correlation with the target category and the minimum redundancy among each other. The ReliefF algorithm is utilized to rank variables in descending order. Final variables importance is ascertained on the basis of the average weighs of importance of variables and contributions of variables to the model's classification error rate. Correlation analysis (CA) is implemented to verify the rationality of the selected variables. Finally, a subset of six variables is served as the input variables to establish three kinds of models. Results indicate that the coupled feature selection method outperforms the single feature selection method, and the proposed wk NN model based on the coupled feature selection method is superior to the other two models and achieves desirable diagnostic performance on both experimental and practical data. [ABSTRACT FROM AUTHOR]

Highlights • Load signatures are formulated based on first three odd harmonic currents. • Harmonic currents' phase angles are measured and taken into account. • Measurements in a real residential installation have been performed. • Three alternative load signatures formations are examined. • Results indicate high NILM performance under the consideration of the phase angles. Abstract In the smart home context, one of the features that most users would desire is the knowledge of the operation of each electrical appliance without the need to install expensive smart plugs or measuring equipment at each outlet-appliance. The Non-Intrusive Load Monitoring (NILM) idea is an alternative method where only one meter is utilized. However, the accuracy and simplicity of NILM approaches are still under improvement and evolvement. In this paper we propose the development of load signatures by a limited number of harmonic current vectors. The rationale behind this is that simultaneously operating appliances may have opposite or displaced harmonic current vectors, which means that using only current amplitudes as load signatures will not result in reliable identification. Three alternative formations are examined aiming to highlight the contribution of the harmonic current phase angles to the robustness of the load signatures. The first formation considers only the current amplitude, the second takes into account the phase angles in the form of vector projection on the x -axis, and the third is based on harmonic current vectors. The respective disaggregation schemes are also proposed considering either amplitude summations or vector aggregations, in order to examine the correct appliance combination that matches the actual operating one. Measurements have been performed in a typical residential installation considering both stand-alone operation of appliances and scenarios with appliance combinations. The results indicate that disaggregation performance is significantly improved when relying on harmonic current vectors in respect to the case with only current amplitudes, which makes the methodology potentially suitable for the new smart meters that are expected to be widely installed. [ABSTRACT FROM AUTHOR]

Abstract: A building is permanently in thermodynamic non-equilibrium due to changing weather, free gains and indoor temperature set-point. Load calculation in dynamic conditions is an essential goal of building energy simulation. This paper demonstrates that the load calculation is a control problem. Supposing that the thermal model of the building is linear and that the model of the building, the weather conditions and occupational program are known in the design stage, the paper proposes an unconstrained optimal control algorithm which uses feed-forward to compensate the weather conditions and model predictive programming (MPP) for set-point tracking. MPP is obtained by modifying the dynamic matrix control (DMC), a variant of model predictive control (MPC). The peak load depends on the set-back time of the indoor temperature: smaller the set-back time, larger the peak load, but smaller energy consumption. Then, the choice of the weighting coefficients in the model predictive programming may be done on economical considerations. [Copyright &y& Elsevier]

Abstract: This paper describes a large-scale wireless and wired environmental sensor network test-bed and its application to occupancy detection in an open-plan office building. Detection of occupant presence has been used extensively in built environments for applications such as demand-controlled ventilation and security; however, the ability to discern the actual number of people in a room is beyond the scope of current sensing techniques. To address this problem, a complex sensor network is deployed in the Robert L. Preger Intelligent Workplace comprising a wireless ambient-sensing system, a wired carbon dioxide sensing system, and a wired indoor air quality sensing system. A wired camera network is implemented as well for establishing true occupancy levels to be used as ground truth information for deriving algorithmic relationships with the environment conditions. To our knowledge, this extensive and diverse ambient-sensing infrastructure of the ITEST setup as well as the continuous data-collection capability is unprecedented. Final results indicate that there are significant correlations between measured environmental conditions and occupancy status. An average of 73% accuracy on the occupancy number detection was achieved by Hidden Markov Models during testing periods. This paper serves as an exploration to the research of ITEST for occupancy detection in offices. In addition, its utility extends to a wide variety of other building technology research areas such as human-centered environmental control, security, energy efficient and sustainable green buildings. [Copyright &y& Elsevier]

Abstract: The Cooling, Heating, and Power (CHP) systems have been widely recognized as a key alternative for thermal and electric energy generation because of the outstanding energy efficiency, reduced environmental emissions, and relative independence from centralized power grids. Nevertheless, the total energy cost of CHP systems can be highly dependent on the operation of individual components. This paper presents an energy dispatch algorithm that minimizes the cost of energy (e.g., cost of electricity from the grid and cost of natural gas into the engine and boiler) based on energy efficiency constrains for each component. A deterministic network flow model of a typical CHP system is developed as part of the algorithm. The advantage of using a network flow model is that the electric and thermal energy flows through the CHP equipment can be readily visualized allowing for easier interpretation of the results. This algorithm has been used in simulations of a case study on the operation of an existing micro-CHP system. The results from the simulation are presented in the paper to demonstrate the economical advantages resulting from optimal operation. [Copyright &y& Elsevier]

[Display omitted] • We designed a low-resolution time-of-flight system for accurate occupancy counting. • The system handles multiple people walking in arbitrary directions. • The system is evaluated in realistic simulations and a physical installation. Understanding the locations of occupants in a commercial built environment is critical for realizing energy savings by delivering lighting, heating, and cooling only where it is needed. In this paper, we present an indoor occupancy counting system using a sparse array of inexpensive, low-resolution, and privacy-preserving time-of-flight sensors. We develop and validate an algorithm for zonal occupancy counting that can deal with multiple people walking underneath the sensors in arbitrary directions, and evaluate the system both in realistic simulations of office spaces and in a real-world installation. We found that our system has an error rate of around 0.4%, resulting in highly accurate person localization and zone counting using only a few sensors per space. [ABSTRACT FROM AUTHOR]

This paper describes a pattern recognition algorithm for determining days of the week with similar energy consumption profiles. The algorithm determines energy use features, such as average daily consumption or peak daily consumption, from time series of energy use. Features are transformed to remove the effects of seasonal variation that may be present in time series data. Then, the transformed features are grouped by day of the week into seven clusters. Univariate and multivariate outlier analysis methods are used to remove unusual data from the seven clusters. Finally, a modified agglomerative hierarchical clustering algorithm determines days of the week with similar energy consumption profiles. Knowledge of days of the week with similar energy consumption profiles can be used in the following ways: (1) supervisory control strategies that use forecasting algorithms, and (2) methods for detecting abnormal energy consumption in buildings. This paper contains field tests results from three buildings. [Copyright &y& Elsevier]

This paper presents a hybrid building modelling method with a reduced modelling and calibration effort. The method combines a physics-based model, which describes the general behaviour of the system, with a machine learning algorithm trained to correct the physics-based model’s systematic errors. To exemplify the method, a highly simplified grey-box model is used as the physics-based part and a Gaussian process as the machine learning part. It is shown that the hybrid model improves the temperature and energy predictions of the grey-box model while having a lower generalization error than the pure Gaussian process. Specifically, the hybrid approach achieved a day-ahead zone temperature prediction error ca. 0.1 K (RMSE) lower than the grey-box model. As for the energy prediction, the hybrid model obtained an error of 3% compared to 8% for the grey-box model. In comparison to the Gaussian process, the hybrid approach achieved better predictions in all cases. The improvements were especially high when the models were trained with small datasets: 0.7 K in the temperature prediction and 25 percentage points in the energy prediction. [ABSTRACT FROM AUTHOR]

This paper investigated the performance of a regenerative heat and mass exchanger (RHMX) for indirect evaporative cooling. A numerical model for RHMX was developed and validated with experimental data from the literature. Then, a data-driven model based on artificial neural network (ANN) algorithm was presented which was derived from the simulation results generated from the numerical model. The comparison between ANN prediction and experiment data showed good prediction accuracy. The average prediction error between the predicted and tested data was around 4% based on the air temperature change across the dry channel. With the data-driven model, parametric analyses were made to investigate the performance of the RHMX under different operating conditions. Finally, a design optimisation of the extraction air ratio was conducted under different ambient conditions. It was found that the optimal extraction air ratio decreased with the ambient temperature and/or relative humidity which ranged from 0.3 to 0.36. [ABSTRACT FROM AUTHOR]

This paper presents an ensemble algorithm based on a new load decomposition method to forecast short-term metropolitan-scale electric load. In this method, a decision tree for hourly seasonal attributes and a weighted average method for daily seasonal attributes are first applied to divide seasons into a completely different way. Then, the load of transition seasons is chosen as a basic component according to power load characteristics, and the differences between total load and the basic component are extracted as the weather-sensitive component. Finally, a time-series method is selected to forecast the basic component and SVM (Support Vector Machine) to the weather-sensitive component. This paper takes the annual electricity load of Shanghai as a case study to verify this ensemble method. The results show that compared with the traditional model based on overall daily load and other load decomposition methods—EMD (Empirical Mode Decomposition) and WT (Wavelet Transform), this ensemble model reduces the error from 3 to 5% to lower than 2% when forecasting the power load of workdays, and for non-work days, the error is decreased from 4 to 5% to lower than 4%. [ABSTRACT FROM AUTHOR]

This paper proposes a systematic modelling and optimizing of energy consumption and indoor thermal comfort for air-conditioning and mechanical ventilation (ACMV) systems. The models of extreme learning machines (ELM) and neural networks (NN) are established and evaluated. These well-trained models are then integrated with the computational intelligence techniques of sparse firefly algorithm (sFA) and sparse augmented firefly algorithm (sAFA). The sFA and sAFA aim to locate the global optimal operating points of the ACMV systems in real-time and predict energy saving rate (ESR) with a third order polynomial regression based on minimizing the mean squared errors (MSE) of the cost functions. This study also covers different indoor scenarios, such as general offices, lecture theatres and conference rooms. Given the well trained models, the maximum prediction of potential ESR can be −30% via the sparse AFA optimizations while maintaining indoor thermal comfort in the pre-defined comfort zone. [ABSTRACT FROM AUTHOR]

An electric meter is used for measuring the electricity consumption in a household. The obtained total data doesn’t separate the consumptions of individual appliances and provides little actionable information for energy efficiency improvement. This paper presents an enhanced Iterative Self-Organizing Data Analysis (ISODATA) algorithm which can identify the operating modes and electricity consumptions of major appliances based on the metered total electricity consumption. The enhanced ISODATA algorithm is improved with k-medoids and parameter selections. It can overcome various drawbacks of the original k-means and the original ISODATA algorithm. Validated with actual data, the presented method is able to disaggregate the total electricity consumption efficiently and accurately into categories corresponding to major end uses. [ABSTRACT FROM AUTHOR]

In this paper the authors present an algorithm that abstracts an arbitrarily shaped set of building volumes into a group of simplified ‘shoebox’ building energy models. It is shown that for generic perimeter and core floorplans the algorithm provides a faster but comparably accurate simulation results of annual load profiles vis-à-vis multi-zone thermal models generated according to ASHRAE90.1 Appendix G guidelines. Envisioned applications range from rapid thermal model generation for urban building energy modelling to schematic architectural design. Following a description of the algorithm, its ability to produce load profiles for a mixed-use neighborhood of 121 fully conditioned buildings for a variety of climates is demonstrated. The comparison yields relative mean square errors in simulated annual building energy use intensity of five to 10 percent compared to ASHRAE 90.1 compliant building energy models while reducing simulation times by a factor of 296. [ABSTRACT FROM AUTHOR]

This paper presents a context-awareness system that uses a thermal camera to monitor energy wastage due to user carelessness in a large enclosed space. The users of the system can receive quantitative information of energy use and identify whether to turn an object on or off. The system is composed of a two-axis robotic arm that can manipulate the angle of a thermographic camera and software that can stitch together images received from various angles; and a context-awareness algorithm to identify objects’ situation in stitched thermographic images. The system’s performance was tested for an individual object, more than two objects close to each other, an object at long distance, and the effect of an obstacle. The context-awareness was successful except when obstacles interfered with observations. However, when obstacles interfered, the context-awareness was successful when their heat adsorption was high or when the insulation leaked. The range of spaces and situations that the system can cover will increase if it is combined with an automated device that can move it horizontally. [ABSTRACT FROM AUTHOR]

Very little work has been done on the feasibility of Machine Learning (ML) for predicting buildings energy demand right at the design stage. This feasibility, if proven, would help to avoid the construction of inefficient buildings. This paper uses dataset from 7559 buildings, and estimates their energy consumption using nine ML models. Results show that deep neural network (DNN) is the most efficient ML model with MAE, MSD and RMSE of 0.93, 1.12 and 1.06 respectively achieved in less than 7 s despite the huge data size. Its r 2 is also the highest (0.96) which means that the DNN approach manages to explain 96% of the energy consumption in buildings and only 4% remains unexplained certainly due to the limitation of independent variables. Also, this result is not affected by building clusters nor by data from a particular climate zone. As an innovation, this study proposes a model that professionals could use in the design phase of a construction project. This model will allow them to take into account all crucial aspects of the design of an energy efficient building. The model will then serve as a decision-making tool to control and optimise the project and to anticipate energy consumption even before the building is constructed. [ABSTRACT FROM AUTHOR]

The area of projected shadow on building facades may play a role of paramount importance in the prediction of solar energy conversion and building thermal and energy performance. Algorithms for solar shading calculations may directly affect the accuracy of results obtained by building energy simulation tools. In addition, the consideration of uncertain design parameters imply an estimative of a confidence interval of solar fraction results that determine the reliability as well as the improvement of shading calculation models. In this way, this paper presents a methodology for uncertainty and sensitivity analysis applied to the prediction of sunlit area on building exterior surfaces with shading devices. From a single building model, the solar fraction was investigated for two cities and four typical days. According to the obtained results, more than 20% of all simulation time steps have uncertainties equal or higher than 5% on the solar fraction calculation at 95% confidence level. The maximum values of the uncertainties occur in warmer months, which could significantly impact the building energy balance. Three inputs have higher influence on the uncertainties on solar fraction results: (1) latitude, (2) orientation and (3) width of overhang fins. Perturbation of only 5° on the building orientation may imply important uncertainties on solar fraction, which is normally neglected by designers. The results highlight the importance of assessing the uncertainty of inputs in algorithms such as the one to calculate the surface solar fraction that is an important prerequisite of the final building energy balance. [ABSTRACT FROM AUTHOR]

This paper proposes a new Maximum Power Point Tracking (MPPT) technique for photovoltaic (PV) systems especially under Partial Shading (PS). This technique is based on Generalized Pattern Search (GPS) optimization algorithm. The proposed technique adopts the GPS algorithm owing to the reason that its major features like simple implementation, derivative free nature, fast convergence and high accuracy are ideally suited to deal with PS conditions. Furthermore, these features of GPS can well resolve the limitations and drawbacks of previous MPPT techniques, such as fluctuation, low efficiency, high complexity and slow convergence. For comparison, our proposed MPPT algorithm, the Particle Swarm Optimization (PSO) algorithm and the Perturb and Observe (P&O) algorithm have been implemented in MATLAB/Simulink. The comparison results demonstrate that the proposed MPPT technique can efficiently detect the global maximum (GM) in a fewer number of voltage samples, which is nearly 4 times less than the number of voltage samples required by PSO. Moreover, the steady state efficiency of the proposed MPPT is 99.8%, which is higher than the other MPPT techniques. Therefore, the proposed technique distinguishes itself from other MPPT techniques due to its superior quality in terms of convergence speed, dynamic and steady state efficiencies and low complexity. [ABSTRACT FROM AUTHOR]

There is a growing trend for the use of thermo-active building systems (TABS) for the heating and cooling of buildings, because these systems are known to be very economical and efficient. However, their control is complicated due to the large thermal inertia, and their parameterization is time-consuming. With conventional TABS-control strategies, the required thermal comfort in buildings can often not be maintained, particularly if the internal heat sources are suddenly changed. This paper shows measurement results and evaluations of the operation of a novel adaptive and predictive calculation method, based on a multiple linear regression (AMLR) for the control of TABS. The measurement results are compared with the standard TABS strategy. The results show that the electrical pump energy could be reduced by more than 86%. Including the weather adjustment, it could be demonstrated that thermal energy savings of over 41% could be reached. In addition, the thermal comfort could be improved due to the possibility to specify mean room set-point temperatures. With the AMLR, comfort category I of the comfort norms ISO 7730 and DIN EN 15251 are observed in about 95% of occasions. With the standard TABS strategy, only about 24% are within category I. [ABSTRACT FROM AUTHOR]

The paper is concerned with the development and evaluation of control algorithms for the implementation of demand response strategies in a smart-grid enabled all-electric residential building. The dwelling is equipped with a 12 kW heat pump, a 0.8 m 3 water storage tank, a 6 kW photovoltaic (PV) array, solar thermal collectors for domestic hot water heating and an electric vehicle. The building, located in Ireland, is fully instrumented. An EnergyPlus building simulation model of the dwelling was developed and calibrated using monitored data from the building. The developed model is used to assess the effectiveness of demand response strategies using different time-of-use electricity tariffs in conjunction with zone thermal control. A reduction in generation cost (−22.5%), electricity end-use expenditure (−4.9%) and carbon emission (−7.6%), were estimated when DR measures were implemented and compared with a baseline system. Furthermore, when the zone control features were enabled, the efficiency of the control improved significantly giving, an overall annual economic saving of 16.5% for the residential energy cost. The analysis also identified an annual reduction of consumer electricity consumption of up to 15.9%, lower carbon emissions of 27% and facilitated greater utilisation of electricity generated by grid-scale renewable resources, resulting in a reduction of generation costs for the utility of up to 45.3%. [ABSTRACT FROM AUTHOR]

Integrated building design is inherently a multi-objective optimization problem where two or more conflicting objectives must be minimized and/or maximized concurrently. Many multi-objective optimization algorithms have been developed; however few of them are tested in solving building design problems. This paper compares performance of seven commonly-used multi-objective evolutionary optimization algorithms in solving the design problem of a nearly zero energy building (nZEB) where more than 1.6 10 solutions would be possible. The compared algorithms include a controlled non-dominated sorting genetic algorithm with a passive archive (pNSGA-II), a multi-objective particle swarm optimization (MOPSO), a two-phase optimization using the genetic algorithm (PR_GA), an elitist non-dominated sorting evolution strategy (ENSES), a multi-objective evolutionary algorithm based on the concept of epsilon dominance (evMOGA), a multi-objective differential evolution algorithm (spMODE-II), and a multi-objective dragonfly algorithm (MODA). Several criteria was used to compare performance of these algorithms. In most cases, the quality of the obtained solutions was improved when the number of generations was increased. The optimization results of running each algorithm 20 times with gradually increasing number of evaluations indicated that the PR_GA algorithm had a high repeatability to explore a large area of the solution-space and achieved close-to-optimal solutions with a good diversity, followed by the pNSGA-II, evMOGA and spMODE-II. Uncompetitive results were achieved by the ENSES, MOPSO and MODA in most running cases. The study also found that 1400–1800 were minimum required number of evaluations to stabilize optimization results of the building energy model. [ABSTRACT FROM AUTHOR]

Among the strategies for borehole heat exchanger (BHE) field design, the Ashrae method is a powerful and fast procedure for the prediction of the BHE field length required by the building monthly heat load profile. The method needs the calculation of the Temperature Penalty, a key parameter that takes into account the error introduced by the infinite cylindrical source (ICS) solution of the conduction problem with respect to the “true” g-function solution. Recently the Authors proposed a modification of the original Ashrae (Kavanaugh and Rafferty) method which maintains the simplicity of the original Ashrae model while dramatically improving the estimations of the required BHE field length and geometry without calculating any g-function and with no need of special algorithms or computer codes. The new method (T p8 method) is centred on a scheme where eight heat sources are thermally interfering with the current BHE source: optimum constants and new model validation had been calculated with reference to a set of more than 200 BHE design configurations described by the pertaining temperature response factors (i.e. g-functions). In this paper the validation of the T p8 model is further carried on in order to prove the model robustness while the design input quantities (BHE depth, ground properties, mutual importance of Ashrae three load sequences) are changed to cover most of practical cases in borefield design. In particular a further simple term is introduced (and discussed) in T p8 Fourier number calculation which enables the present model to assure high accuracy also when the BHE depth is significantly different from usual values, say from short to very deep BHEs (60-240 meters). [ABSTRACT FROM AUTHOR]

In this paper a building occupancy prediction method is presented, which is based on the spatio-temporal analysis of historical data (occupancy modelling) and further relies heavily on current contextual information, being therefore suitable for providing real-time prediction. Two different algorithmic approaches are proposed, based on Markov models, revealing how context awareness adds the capability of rapidly adjusting to current conditions and capturing unexpected events, as opposed to capturing only typical occupancy fluctuation expected on a regular basis. Both proposed approaches are evaluated against accurate real-life data collected from a tertiary building, achieving notable results which outperform currently used methods. [ABSTRACT FROM AUTHOR]

This paper presents a new method based on a sensor network for designing a lighting control system for an industrial building. An illumination mapping between the work plane and the horizontal plane was derived for sensor placement to generate rules for sensor layout. Online sensor data sampling and a control center database with a real-time update algorithm were applied to lessen the effects of interference and luminous decay. We implemented the method for the lighting system of an industrial building in Xi’an, China. Skylights combined with artificial lighting were designed for daytime lighting. The optimized placement and a scene-control strategy for the artificial lighting system were generated by simulations with DIALux and Autodesk Ecotect Analysis. Daily lighting consumption data for August, 2014, demonstrated that the control system strategy realized lighting energy savings of up to an average of 80% on cloudy days. The design can be used as a reference for new industrial construction or retrofit applications. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of the transition between two constant temperature setpoints in small buildings. The proposed method consists of finding a trajectory resulting in significant reduction in the peak thermal load during the transition period. This trajectory should reach and maintain the new setpoint without a subsequent “load rebound”. An optimal transition path, which serves as a benchmark, is obtained through an optimization algorithm. A trajectory that can be adjusted with a single “curvature” parameter is then proposed. The value of the curvature parameter yielding the smallest peak is found numerically and a “near-optimal curve” is thus achieved. The properties of this curve are investigated. For the particular case examined, the near-optimal curve yields a peak load about 8% smaller than that corresponding to a linear ramp. A general expression for the curvature parameter of the near-optimal curve is obtained by performing parametric runs. The general formulation is then given as a function of two parameters: (a) the building time constant; (b) and the user-required transition time. The proposed method, suitable for a programmable thermostat, allows achieving considerable peak reductions in an inexpensive and practical manner, without requiring an optimization for each particular case. [ABSTRACT FROM AUTHOR]

This paper provides a comparative study of state-of-the-art means of predicting occupancy for smart heating control applications. We focus on approaches that predict the occupancy state of a home using occupancy schedules – that is, past records of the occupancy state. We ran our analysis on actual occupancy schedules covering several months for 45 homes. Our results show that state-of-the-art, schedule-based occupancy prediction algorithms achieve an overall prediction accuracy of over 80%. We also show that the performance of these algorithms is close to the theoretical upper bound expressed by the predictability of the input schedules. Building upon these results, we used ISO 13790-standard modelling techniques to analyse the energy savings that can be achieved by smart heating controllers that use occupancy predictors. Furthermore, we investigated the trade-off between achievable savings (typically 6–17% on average) and the risk of comfort loss for household residents. [ABSTRACT FROM AUTHOR]

The installation of smart meters has provided an opportunity to better analyze residential energy consumption and energy-related behaviors. Air-conditioning (A/C) use can be determined through non-intrusive load monitoring, which separates A/C cooling energy consumption from whole-house energy data. In this paper, a disaggregation technique is described and executed on 1-min smart meter data from 88 houses in Austin, TX, USA, from July 2012 through June 2013. Nineteen houses were sub-metered to validate the accuracy of the disaggregation technique. The R 2 value between the predicted and actual A/C energy use for the 19 houses was 0.90. The algorithm was then applied to all houses. On average, daily energy use from A/C increased by 25 ± 11 kWh between a mild temperature day of 15.5 °C (60 °F) and a hotter day of 31.5 °C (89 °F), with an 11 kWh increase just during peak hours (14:00–20:00). Average time operated, number of cycles, and A/C fraction of energy were found to increase linearly with outdoor temperature up to 25 °C (77 °F); a plateau was detected at higher temperatures. The accuracy of A/C disaggregation on 5-min data was found to be comparable to 1-min data. However, 15-min data did not yield accurate results due to insufficient granularity. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we outline a framework and the algorithms that will enable a home controller to accomplish occupancy-moderated multi-zone, multi-inhabitant space-conditioning under a demand-driven pricing scheme for electricity. We investigated the effects that influencing factors such as the number of zones and inhabitants, randomness of occupancy patterns and thermal mass of the residence can have on the efficacy of occupancy-moderated zonal space-conditioning (OZS) under different scenarios. Simulations driven by real data and highly realistic proxy data reveal that OZS can achieve improvements averaging 23% over the case without under certain settings. Our investigations also revealed that a house with heavier thermal mass stands to gain as much as 15% from OZS with pre-conditioning than a lighter one. We further offer a few pointers on reducing the cost of space-conditioning using OZS under a demand-driven pricing scheme to the average homeowner based on our findings. [Copyright &y& Elsevier]

Abstract: Designing low-energy buildings has become a necessity, encouraged by thermal regulations, the need for energy savings and environmental awareness. Computer-aided thermal design of building walls is currently investigated using the latest optimization algorithms. This paper studies building multilayer walls by coupling a direct thermal model with a specific optimization algorithm. The direct problem solution is based on the Laplace transform of the quadrupole method, and then translated by numerical inversion into the time domain by the Fourier series method. The optimization model minimizes a least squares criterion between intended indoor temperatures and a direct response model. The work aims to optimize the thermal insulation and the heat capacity of wall layers and further building heating loads. An indoor temperature evolution is specified under fixed outdoor conditions in order to identify the composition of the building walls using an inverse resolution based on a reflective Newton algorithm applied to a direct quadrupole model. [Copyright &y& Elsevier]

Abstract: Integrated Room Automation (IRA) for office buildings deals with the automated control of blinds, electric lighting, heating, cooling, and ventilation of a room or building zone. Model Predictive Control (MPC) presents a promising method to achieve energy savings thanks to improved control. This paper presents the development and validation of an intermediate complexity, integrated building and building equipment model suitable for application within MPC. A resistance-capacitance (RC) modeling approach was chosen. The result is a 12th order multiple-input-multiple-output bilinear model for the coupled simulation of the thermal, light and air quality dynamics of a single room. It includes the subsystems mechanical and natural ventilation, radiator and floor heating, cooling ceiling and thermally activated building system. For computational efficiency and compatibility with MPC, several approximations and a reduction of complexity were necessary. The resulting behaviors were validated against detailed building and system simulations. The model was found to deliver accurate and reliable results. It can be flexibly configured to represent typical building types and variants of technical systems. Furthermore, it is sufficiently efficient to support large-scale sensitivity studies. Finally, the model is ready for adaptation and integration into commercial algorithms for the control of real buildings. [Copyright &y& Elsevier]

Abstract: Current building energy simulation technology requires extensive labor, time and expertise to create building energy models, substantial computational time for accurate simulations, and generates data in formats that make results difficult to interpret. These deficiencies can be ameliorated using modern graphical user interfaces and algorithms which take advantage of modern computer architectures and display capabilities. This paper describes a novel test bed environment which offers an interactive graphical interface, provides access to simulation modules that run at accelerated computational speeds, and presents new graphic visualization methods for the interpretation of simulation results. Its modular structure makes it suitable for use in early stage building design, for use as a research platform for the investigation of new simulation methods, and for use as a tool for teaching concepts of sustainable design. Improvements in the accuracy and execution speed of many of the simulation modules are based on the modification of advanced computer graphics rendering algorithms. Significant performance improvements are illustrated in several computationally expensive energy simulation modules. [Copyright &y& Elsevier]

Abstract: This paper presents a computational analysis on the turbulent flow and heat transfer for water flowing in a geothermal vertical U-tube heat exchanger (GHE). The governing equations, based on the k–ɛ model used to describe the turbulence phenomena, are solved by using finite volume method. The velocity and pressure terms of momentum equations are solved with SIMPLEC algorithm. The simulation of the dynamic and thermal behavior of the geothermal vertical U-tube heat exchanger was carried out under Fluent software. Results are compared with recent work from the literature and good agreement is obtained. The evolution of temperature and the heat transfer according to time (700h) and position, have been fixed, as well as the influence of fluid flow on the dynamic and thermal behavior of the heat exchanger. [Copyright &y& Elsevier]

Abstract: An integrating air-handling unit (IAHU) control theory has been proposed to improve the energy efficiency in office buildings by utilizing the regional and operation differences among multiple AHUs. Unlike the conventional AHU operation, where the units are controlled as independent systems without interaction, IAHU coordinates the AHUs based on the dynamic outside air conditions and system operation modes to achieve synergized energy performance and maintain the indoor air quality. The synergization strategy allows the outside air intake and the airflows to be orderly re-allocated among the AHUs when conditions are appropriate. This paper presents the implementation methodology and performance evaluation of IAHU in an open-plan office building with multiple AHUs. The allocation of airflows among the AHUs is described first to illustrate how IAHU deals with multiple AHUs in a building. The supervisory level control algorithm is then detailed and easy-to-follow flowcharts are provided based on the decision-making schema. A two-step hourly evaluation method and the energy simulation model are introduced. An office building with multiple AHUs is selected to assess the performance of IAHU. The study concludes that the innovative IAHU with the easy-to-implement strategy can be readily implemented to achieve high energy efficiency in open space office buildings. [Copyright &y& Elsevier]

Abstract: Controllers of HVAC systems are expected to be able to manipulate the inherent nonlinear characteristics of these large scale systems that also have pure lag times, big thermal inertia, uncertain disturbance factors and constraints. In addition, indoor thermal comfort is affected by both temperature and humidity, which are coupled properties. To control these coupled characteristics and tackle nonlinearities effectively, this paper proposes an online tuned Takagi–Sugeno Fuzzy Forward (TSFF) control strategy. The TS model is first trained offline using Gauss–Newton Method for Nonlinear Regression (GNMNR) algorithm with data collected from both building and HVAC system equipments. The model is then tuned online using the gradient algorithm to enhance the stability of the overall system and reject disturbances and uncertainty effects. As control objective, predicted mean vote (PMV) is adopted to avoid temperature–humidity coupling, thermal sensitivity and to save energy at the same time. The proposed TSFF control method is tested in simulation taking into account practical variations such as thermal parameters of buildings, weather conditions and other indoor residential loads. For comparison purposes, normal Takagi–Sugeno fuzzy and hybrid PID Cascade control schemes were also tested. The results demonstrated superior performance, adaptation and robustness of the proposed TSFF control strategy. [Copyright &y& Elsevier]

Abstract: The aim of this paper is to present an optimal control algorithm to manage borehole thermal energy storage systems (BTES). Such a system gets exhausted, if it is employed intensively, i.e. outlet fluid is outside acceptable temperature ranges, and can no longer provide the desired heat or cold. To avoid this problem a control algorithm is proposed, which simultaneously optimizes the operation costs. This algorithm is based on a dynamic programming technique and results in an array which provides the optimal heat flux for a given field temperature, date and demand. The controller is illustrated on a simulation of an existing building. Several weather scenarios have been examined and the system remained robust under all situations. [Copyright &y& Elsevier]

Abstract: A robust fault detection and diagnosis (FDD) strategy using a hybrid approach is presented for pressure-independent variable air volume (VAV) terminals in this paper. The residual-based cumulative sum (CUSUM) control charts are utilized to detect faults in VAV terminals. The residuals between the temperature error and its predication are generated using autoregressive time-series models. The standard CUSUM control charts are used to monitor the residuals which are statistically independent. If the CUSUM value exceeds the chart limits, it means the occurrence of fault or abnormity in the corresponding VAV terminal. The residual-based CUSUM control chart can improve the accuracy of fault detection through eliminating the effects of serial correlation on the performance of control charts. Also, the residual-based CUSUM control chart can enhance the robustness and reliability of fault detection through reducing the impacts of normal transient changes. A rule-based fault classifier consisting of expert rules and fault isolation algorithms is developed to isolate 15 fault sources. The FDD strategy was online tested and validated using in real time data collected from real VAV air-conditioning systems. [Copyright &y& Elsevier]

Abstract: A new decision support system for the integrated assessment of thermal insulation solutions with emphasis on recycling potential is presented in this paper. The suggested system comprises three main assessment factors: primary energy consumption, the environmental impact and the financial cost; each and every factor is analytically assessed during the four distinct stages of a thermal insulation solution''s life cycle. The calculation of the aforementioned factors takes place using analytical algorithms, formulated in such a way that have led to the development of the integrated, holistic decision-making support tool, namely ib3at. Using ib3at it becomes feasible to optimize the end-of-life management of thermal insulation solutions, but also to select, during the design phase of a new building, the optimal thermal insulation solution for each building element. The ib3at is applied for common thermal insulation practices, used widely in new constructions as well as in the renovation of existing buildings. [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]

Abstract: Monthly peak demand costs usually contribute greatly to the monthly electricity bills of commercial buildings. Peak demand limiting control, which gained substantial attention recently, is an efficient way to reduce it. Most of previous studies either focus on the daily peak demand reduction without taking account the related energy rise, or explore the relationship between energy rise and demand reduction only on a daily basis. Unlike the previous studies, a new demand limiting control strategy is proposed in this paper in order to maximize the monthly cost saving. The new strategy is realized as follows. At first step, a proper monthly demand threshold is identified. At second step, a specific approach, named as proportional-integral-derivative (PID) algorithm, is implemented to restrain the daily peak demand to the given threshold by adjusting the indoor room temperature set-point. The extended pre-cooling duration is also estimated at this step based on the difference between the predicted daily peak demand and the identified threshold. The results of case studies show that the proposed strategy can substantially reduce the monthly electricity cost. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a predictive control structure for thermal regulation in buildings. The proposed method exploits the intermittently operating mode of almost all types of buildings. Usually the occupation profile can be known in advance and this fact will be used to reduce the energy consumption without decreasing the thermal comfort during the occupation. For that purpose, the predictive control strategy is first presented for a single zone building then extended to a multizone building example. Two opposite control strategies commonly exists: the decentralized control structure, which does not offer good performances especially when the thermal coupling among adjacent rooms is not negligible, and on the other hand, the centralized control for which the computational demand grows exponentially with the size of the system, being very expensive for large scale buildings. Our solution is based on a distributed approach which takes the advantages of both methods mentioned above. A distributed MPC algorithm with one information exchange per time step is proposed with good control performances and low computational requirements. Simulations and a comparison performance table end the article. [Copyright &y& Elsevier]

Abstract: An Earth-to-Air Heat Exchanger (ETAHE) is a low energy cooling and heating building component. It uses the ground''s thermal storage to dampen ambient air temperature oscillations by delivering the air through a horizontally buried duct. To reduce airflow resistance, some hybrid ventilated buildings have recently adopted large cross-sectional area ducts. This paper describes the development of an Artificial Neural Network based Heat Convection (ANN-HC) algorithm to predict local average Nusselt Numbers along the duct surfaces. Furthermore, the ANN-HC algorithm is integrated with a transient three-dimensional heat transfer model based on finite element analysis of heat conduction in the ground domain surrounding the ETAHE to establish a new thermal modeling method for ETAHEs. A case study is presented to demonstrate the working principle of the new method. It is shown that the method can very well simulate the interactions between an ETAHE and its environment. [Copyright &y& Elsevier]

Abstract: This paper presents a strategy for improving the reliability and the energy efficiency of chiller sequencing control based on the total cooling load measurement of centralized multiple centrifugal chiller plants. The improvement is achieved as follows. Firstly, a fused measurement of building cooling load is used to replace the direct/indirect measurement. Secondly, the maximum cooling capacity of individual chillers is computed online using a simplified centrifugal chiller model. Thirdly, the online computed maximum cooling capacity is calibrated according to the quality of the fused measurement in order to deal with the possible misbehaviours in measurement instruments. A simplified model for computing the maximum cooling capacity is developed and validated using field data. The performance of the proposed chiller sequencing control strategy is tested and compared with a conventional chiller sequencing control algorithm. Test results are presented showing that the proposed strategy can effectively improve the reliability of chiller sequencing control and reduce the energy consumption of chiller plants. [Copyright &y& Elsevier]

Abstract: A new module was developed for and implemented in the EnergyPlus program for the simulation and determination of the energy impact of thermal chimneys. This paper describes the basic concepts, assumptions, and algorithms implemented into the EnergyPlus program to predict the performance of a thermal chimney. Using the new module, the effects of the chimney height, solar absorptance of the absorber wall, solar transmittance of the glass cover and the air gap width are investigated under various conditions. Chimney height, solar absorptance and solar transmittance turned out to have more influence on the ventilation enhancement than the air gap width. The potential energy impacts of a thermal chimney under three different climate conditions are also investigated. It turned out that significant building cooling energy saving can be achieved by properly employing thermal chimneys and that they have more potential for cooling than for heating. In addition, the performance of a thermal chimney was heavily dependent on the climate of the location. [Copyright &y& Elsevier]