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This paper investigates the cyber resiliency of future power systems with high penetration of distributed energy resources using advanced distributed and (or) hierarchical control architectures. Specifically, we simulate cyberattacks on three prototypical use cases, and we identify attack scenarios that are the most damaging to the overall system performance. We show that these attacks can have a significant impact on grid operation. Results provide additional insights into the robustness of the system to the most common cyberattacks. • This paper delves into the realm of cyber-resiliency, examining its implications across three distinct use cases encompassing distributed, distributed-hierarchical, and hierarchical control architectures. • Through exhaustive numerical simulations, we discern and highlight the precise signals within the system that exhibit heightened susceptibility to cybersecurity attacks. • The results obtained from our analysis offer valuable insights into the robustness of these systems, shedding light on their ability to withstand potential cybersecurity threats. [ABSTRACT FROM AUTHOR]

Building and microgrid designs with highly-distributed electrical storage have potential advantages over today's conventional topologies with centralized storage. This paper studies the capital cost benefits of several residential behind-the-meter distributed-storage topologies, including AC and DC versions of systems with load-packaged batteries and resilient sub-networks. The study begins by defining the block configuration of each topology. This work then develops a model for the cost of the power electronics necessary to interface with the storage elements. Finally, the analysis develops a model for the total cost of each storage topology, incorporating the installation and soft costs. The results suggest that while the cost of power electronics is lower in centralized topologies, the total cost is lower for distributed storage due to the avoided costs of installation and permitting. This paper also explores the benefits of load-packaged batteries for savings in electrical infrastructure. [ABSTRACT FROM AUTHOR]

To explore the bidirectional interaction between renewable energy and buildings in multi-agent energy systems, this paper proposes a distributed cooperative operation strategy for multi-agent energy systems integrated with wind, solar, and buildings based on chance-constrained programming (CCP). First, the multi-agent energy system integrated with wind, solar, and buildings is comprehensively modeled with detailed electric and thermal characteristics for flexibility enhancement. Then for maximizing the profits of the cooperative energy system and each engaged agent, a Nash bargaining model is presented and is divided into two subproblems: the coalition income and the power payment. To preserve the privacy of agents, the adaptive alternating direction method of multipliers (ADMM) is exploited to solve both subproblems. Meanwhile, the CCP method is applied to address diverse uncertainties from wind and solar power generation as well as outdoor temperature. Finally, the effectiveness of the proposed strategy is validated. The simulation results show that, besides the privacy of information among all agents being well preserved, our strategy enhances the profits not only for the energy system but also for all engaged agents. • A distributed cooperative operation strategy for multi-agent energy systems integrated with wind, solar, and buildings is proposed. • Bidirectional interactions between wind/solar power plant and buildings are realized by the Nash bargaining-based incentive cooperative mechanism. • Indoor temperature comfort in buildings is ensured under diverse uncertainties by exploring the building flexibility through chance-constrained programming. • Power trading results and the economy of the energy system and the participating agents are analyzed. [ABSTRACT FROM AUTHOR]

An increasing number of federal and state regulations and financial incentive programs have been enacted in recent years to promote renewable energy systems integration in new buildings. This requires designers to analyze the initial and lifecycle costs of feasible renewable energy systems in order to optimize their integration in buildings during all design phases. The goal of this study is to support designers in generating optimal building schematic designs to maximize the integration and generation of onsite renewable energy. To accomplish this goal, this paper presents the development of a novel multi-objective optimization model that can be used during the schematic design phase to identify optimal building design decisions including dimensions, orientation, location on site, solid-to-void ratio, and the type and size of onsite renewable energy systems. The optimization objectives of the developed model focus on maximizing the generation of onsite renewable energy, maximizing savings-to-investment ratio of all integrated renewable energy, and minimizing both construction and renewable energy costs. An application example of optimizing the schematic design of a 6500 m2 building was analyzed to highlight the model original contributions and its novel capabilities. The results of this analysis highlight the original contributions of the developed model and its novel capabilities that can be used to maximize the generation of onsite renewable energy and its savings-to-investment ratio while minimizing building cost by optimizing building schematic design and renewable energy (RE) technology decisions. • Optimizing building schematic design with renewable energy integration. • Maximizing RE generation, savings-to-investment ration, and minimizing bilding costs. • Complying with building design requirements and renewable energy constraints. • Optimal building design parameters and renewable energy technologies. • Analyzing the impact of design decisions on renewable energy and building costs. [ABSTRACT FROM AUTHOR]

Clustering methods are often used to model energy consumption for two reasons. First, clustering is often used to process data and to improve the predictive accuracy of subsequent energy models. Second, stable clusters that are reproducible with respect to non-essential changes can be used to group, target, and interpret observed subjects. However, it is well known that clustering methods are highly sensitive to the choice of algorithms and variables. This can lead to misleading assessments of predictive accuracy and mis-interpretation of clusters in policymaking. This paper therefore introduces two methods to the modeling of energy consumption in buildings: clusterwise regression, also known as latent class regression, which integrates clustering and regression simultaneously; and cluster validation methods to measure stability. Using a large dataset of multifamily buildings in New York City, clusterwise regression is compared to common two-stage algorithms that use K -means and model-based clustering with linear regression. Predictive accuracy is evaluated using 20-fold cross validation, and the stability of the perturbed clusters is measured using the Jaccard coefficient. These results show that there seems to be an inherent tradeoff between prediction accuracy and cluster stability. This paper concludes by discussing which clustering methods may be appropriate for different analytical purposes. [ABSTRACT FROM AUTHOR]

This paper tests to what extent different types of variables (building factors, socio-demographics, attitudes and self-reported behaviours) explain annualized energy consumption in residential buildings, and goes on to show which individual variables have the highest explanatory power. In contrast to many other studies, the problem of multicollinearity between predictors is recognised, and addressed using Lasso regression to perform variable selection. Using data from a sample of 924 English households collected in 2011/12, four individual regression models showed that building variables on their own explained about 39% of the variability in energy consumption, socio-demographic variables 24%, heating behaviour 14% and attitudes & other behaviours only 5%. However, a combined model encompassing all predictors explained only 44% of all variability, indicating a significant extent of multicollinearity between predictors. Once corrected for multicollinearity, building variables predominantly remained as significant predictors of energy consumption, in particular the dwelling’s size and type. Of the sociodemographic predictors, only the household size remained significant, and of the heating behaviours only the length of heating season was significant. Reported beliefs about climate change were also a significant predictor. The findings indicate that whilst people use energy, it is physical building characteristics that largely determine how much is used. This finding, together with the relatively greater time-invariant nature of building characteristics underlines their importance when focusing on seeking to understand residential energy consumption at a stock level. Retrofitting and behaviour change initiatives remain important avenues to reduce consumption, as suggested through the lower energy consumption associated with full double-glazing and shorter heating season. However, the dominance of building size also indicates that living in appropriately sized buildings is of great importance for energy consumption. The results also indicate that more than half of the variability in energy consumption cannot be explained, even when using a wide range of predictors. The paper also discusses the need to collect better occupant-related variables to give a correct representation of the impact of behaviour, such as heating demand temperatures. Furthermore, choices about dwelling characteristics could also be seen as a type of behaviour, even though it cannot be modelled in a cross-sectional analysis as used in this study. [ABSTRACT FROM AUTHOR]

Highlights • Numerical proof of concept is provided for a novel dynamic PCM building system. • The system is able to modify the position of PCM with respect to the insulation. • The system can not only be used as thermal barrier but as a cooling supplier system. • Activations of the system and melting PCM temperature were optimized. Abstract A novel concept based on the dynamic use of phase change materials (PCM) in building envelopes is presented in this paper. The concept aims at breaking the main technical barriers that PCM have been dealing with in its application as passive cooling system: (i) solidification process of PCM is limited and (ii) peak cooling load is delayed but mainly discharged indoors. The concept relies on the ability of the system to modify the position of the PCM layer inside the building envelope with respect to the insulation layer. A proof of concept evaluation based on a numerical tool demonstrates the cooling load reduction potential of this technology when implemented in different construction systems. PCM peak melting temperature as well as the daily activations of the system were optimized using a Particle Swarm Optimization (PSO) algorithm. The numerical results indicate that the dynamic system facilitates dramatically the solidification process of PCM, allowing the system to be designed with lower PCM peak melting temperatures. The potential of the system to charge PCM which solidifies at temperatures lower than indoor set point, allows the technology to be used not only as a thermal barrier but as a cooling supplier system. [ABSTRACT FROM AUTHOR]

Highlights • A lumped urban-building thermal coupling model is developed. • Lower urban temperatures in naturally-ventilated cities than air-conditioned ones. • Stronger urban cool island in a low-rise sparse city than a high-rise compact one. • Urban heat/cool island changes in a unique 'V' shape/'ʌ' shape under urbanization. Abstract Many cities are suffering the effects of urban heat islands (UHI) or urban cool islands (UCI) due to rapid urban expansion and numerous infrastructure developments. This paper presents a lumped urban-building thermal coupling model which captures the fundamental physical mechanism for thermal interactions between buildings and their urban environment. The benefits of the model are its simplicity and high computational efficiency for practical use in investigating the diurnal urban air temperature change and its asymmetry in a city with both naturally-ventilated (NV) and air-conditioned (AC) buildings. Our model predicts a lower urban heat island and higher urban cool island intensity in a city with naturally-ventilated buildings than for a city with air-conditioned buildings. During the urban densification (from a low-rise, low-density city to a high-rise, high-density one), the increases in the time constant and internal heat gain give rise to asymmetric warming phenomena, which become more obvious in a city with air-conditioned buildings rather than naturally-ventilated ones. Unlike previous studies, we found that a low-rise, low-density city experiences a stronger urban cool island effect than a high-rise, high-density city due to less heat being emitted into the urban atmosphere. The urban cool / heat island effect will firstly increase/decrease, and then rapidly decrease/increase and ultimately disappear/dominate with increasing time constant in the process of urbanization / urban densification. [ABSTRACT FROM AUTHOR]

Highlights • A data driven model was developed to simulate a generic building cluster. • Statistical occupancy models were developed and applied to buildings. • A building portfolio was set up with regard to archetypes and number of households. • The uncertainty of the energy flexibility caused by uncertain occupancy was quantified. Abstract With the growing use of renewable energy sources, the stability of electrical power systems can be seriously affected by fluctuations in the available power. As one of the potential solutions for this new challenge, the energy flexibility of buildings has become a focus for research and technological development. Most studies have focused on single buildings, with only a few studies on building clusters in which the building models were usually oversimplified in that they did not consider different building types or their thermal characteristics, their occupancy or their occupants' behaviour. In this paper, we describe a data driven approach to simulating a generic building cluster that could resemble any mix of building archetypes and occupancy. The energy flexibility potential of apartment building clusters was estimated by using data from surveys and available statistics in Denmark for the worst case scenario, i.e. when the end users do not allow any disturbance when they are at home, so that energy flexibility is only available when residents are not at home. In this scenario, no energy flexibility is assumed when buildings are occupied, which yields a conservative estimation. The uncertainty of the energy flexibility potential due to uncertain occupancy and various archetypes was quantified for different scales of building cluster. The resulting hybrid-model is a combination of a building model and an occupancy model and includes the different factors that influence the potential energy flexibility of buildings. The results show that the uncertainty of the energy flexibility decreases when the aggregated number of buildings increases. The uncertainty of energy flexibility was less than 10%, when about 700 households were aggregated. This approach can be used to simulate building energy flexibility for district or even regional level energy planning when the intention is to use the available flexibility to address the challenges caused by fluctuation in the power available from renewable energy sources. [ABSTRACT FROM AUTHOR]

Highlights • Propose a framework to explore design parameters of informal settlement redevelopment. • Demonstrate need for assessing thermal comfort implications in developing world cities. • Explore 18,900 design scenarios for largest settlement in Asia (Dharavi, Mumbai, India) • A vertical form was most desired design with WWR, ventilation important parameters. • Highlight comfort equity issues and underscore the need for an informed design process. Abstract Cities are of paramount importance to meeting our sustainable energy goals. In particular, the massive informal settlement or "slum" redevelopment programs occurring in many global cities of the developing world represent an incredible opportunity to design dwellings that improve living conditions while putting us on a trajectory towards more energy efficient cities. However, we currently lack an understanding of how redevelopment designs will impact occupant aspects like thermal comfort and energy usage. In this paper, we explore how early-stage design decisions for redevelopment of informal settlements would impact thermal comfort and energy implications in a highly contextualized energy simulation. Specifically, we conduct a first-of-its-kind analysis of the Dharavi informal settlement in Mumbai, India that identifies optimal redevelopment design configurations, explores spatial and temporal thermal heterogeneity and quantifies the impact that specific design parameters have on thermal comfort. In doing so, we aim to establish a novel computational energy modeling framework for exploring the impact that localized design parameters have on informal settlement redevelopment in India and the rest of the world. We model and simulate 18,900 design scenarios in the existing horizontal (M1) and two proposed vertical (M2, M3) building morphologies. Our results indicate that redevelopment plans must be designed carefully since simply replicating current materials and other parameters in a vertical form will likely worsen thermal comfort and associated energy burdens for occupants. Moreover, results revealed that the M3 vertical morphology was the most desired design case as it provided the most "compliant" days (i.e., days in which no dwelling exceeded the upper bound of Indian comfort standards) but thermal comfort equity could be an issue as significant variation exists between units at the ground floor and top floor. The M3 vertical morphology was also found to be the most sensitive form to other building design parameters (e.g., WWR, thermal insulation, ventilation) – underscoring the need for specific design guidelines on other parameters when adopting this form. Deeper sensitivity analysis revealed that window-to-wall ratio (WWR) was the most sensitive design parameter. Additionally, we found that the ventilation rate had as much of an impact on thermal comfort as other design parameters pointing to opportunities to enhance thermal comfort in the operational phase of dwellings. In the end, by establishing a computational energy modeling framework specifically for informal settlements and exploring design parameters for the largest informal settlement in Asia (Dharavi), our work has significant implications for how we can inform informal settlement redevelopment that both enhances occupant living conditions and set our cities' on a pathway to a sustainable energy future. [ABSTRACT FROM AUTHOR]

Highlights • Optimization of district heating based on measurements and numerical simulations. • The optimal scheduling of heating systems connected with the network is obtained. • A clustering analysis is performed to identify the constraints. • Peak demand of the district heating network is significantly reduced. • An application to the Turin network is performed. Abstract In this paper, a method for the optimization of the thermal load of a district heating network acting on the building demand profiles is proposed. The aim of the optimization is to increase the system performance by minimizing the thermal peaks. The simplest way to implement such optimization consists in anticipating the time of the heating systems are switched on, so that the maximum requests do not occur at the same time. Proper constraints are posed in the optimization, so that the thermal comfort in the buildings is not affected. Various practical results can be obtained: (1) reduction of the primary energy consumption, (2) connection of additional buildings to the network despite the presence of hydraulic bottlenecks, and (3) shifting the thermal production of cogeneration plants in order to increase competitiveness on the electricity market. The optimization of the thermal request profile is here performed along with a cluster analysis, in order to catch buildings with similar characteristics. With this aim operating data of various thermal substations connected to a distribution network of a large district heating are analysed. Results show that the clustering approach allows achieving a large peak reduction. The application to six distribution networks indicates that an average reduction of the thermal peak load of about 14% can be obtained, while this reduction is about 8% when optimization is performed without clustering. [ABSTRACT FROM AUTHOR]

Climatic conditions strongly affect the energy performance of buildings, and due to the synergy between global climate change and the Urban Heat Island (UHI) effect, the climatic conditions inside and outside the city are highly different. However, weather data collected at airports are commonly used for building energy simulations and these data do not take into account real temperature distribution in cities. Many studies in the literature address the topic but mostly considering only a couple of urban and non-urban stations. In this paper, the urban climate in Rome, Italy, is analysed after monitoring of the air temperature and relative humidity in four selected neighbourhoods and one reference station from October 2014 until September 2017. Rome is characterised by a composite urban pattern and high variability of building types, of which the four selected areas are representative. The heating degree days decrease up to 18% and cooling degree days increase up to 157% in the urban area with respect to the rural reference and the area most affected by the UHI is the city centre. The UHI is more intense in summer than in winter (average increase between 0.7 °C and 1 °C); while the diurnal and nocturnal UHI depends on the season and the neighbourhood. Then, the energy performance of a representative apartment block and a typical office building was computed using the measured data. Regarding the predicted energy performance, comparing the four urban sites and the reference site, the UHI causes a reduction of heating consumption up to 21% in residential building and 18% in the office building. An increase of cooling consumption up to 74% is instead computed for the residential building and up to 53% for the office building. [ABSTRACT FROM AUTHOR]

Many studies have proven that the building sector can significantly benefit from replacing the current practice rule-based controllers (RBC) by more advanced control strategies like model predictive control (MPC). However, the optimization-based control algorithms, like MPC, impose increasing hardware and software requirements, together with more complicated error handling capabilities required from the commissioning staff. In recent years, several studies introduced promising remedy for these problems by using machine learning algorithms. The idea is based on devising simplified control laws learned from MPC. The main advantage of the proposed methods stems from their easy implementation even on low-level hardware. However, most of the reported studies were dealing only with problems with a limited complexity of the parametric space, and devising laws only for a single control variable, which inevitably limits their applicability to more complex building control problems. In this paper, we introduce a versatile framework for synthesis of simple, yet well-performing control strategies that mimic the behavior of optimization-based controllers, also for large scale multiple-input-multiple-output (MIMO) control problems which are common in the building sector. The approach employs multivariate regression and dimensionality reduction algorithms. Particularly, deep time delay neural networks (TDNN) and regression trees (RT) are used to derive the dependency of multiple real-valued control inputs on parameters. The complexity of the problem, as well as implementation cost, are further reduced by selecting the most significant features from the set of parameters. This reduction is based on straightforward manual selection, principal component analysis (PCA) and dynamic analysis of the building model. The approach is demonstrated on a case study employing temperature control in a six-zone building, described by a linear model with 286 states and 42 disturbances, resulting in an MPC problem with more than thousand of parameters. The results show that simplified control laws retain most of the performance of the complex MPC, while significantly decreasing the complexity and implementation cost. [ABSTRACT FROM AUTHOR]

A variety of novel, recyclable and reusable, construction materials has already been studied within literature during the past years, aiming at improving the overall energy efficiency ranking of the building envelope. However, several studies show that a delicate control of indoor climating elements can lead to a significant performance improvement by exploiting the building’s savings potential via smart adaptive HVAC regulation to exogenous uncertain disturbances (e.g. weather, occupancy). Building Optimization and Control (BOC) systems can be categorized into two different groups: centralized (requiring high data transmission rates at a central node from every corner of the overall system) and decentralized 1 The terms “decentralized”, “distributed” and “agent-based” are considered to have similar meanings herein. 1 (assuming an intercommunication among neighboring constituent systems). Moreover, both approaches can be further divided into two subcategories, respectively: model-assisted (usually introducing modeling oversimplifications) and model-free (typically presenting poor stability and very slow convergence rates). This paper presents the application of a novel, decentralized, agent-based , model-free BOC methodology (abbreviated as L4GPCAO) to a modern non-residential building (E.ON. Energy Research Center’s main building), equipped with controllable HVAC systems and renewable energy sources by utilizing the existing Building Management System (BES). The building testbed is located inside the RWTH Aachen University campus in Aachen, Germany. A combined rule criterion composed of the non-renewable energy consumption (NREC) and the thermal comfort index – aligned to international comfort standards – was adopted in all cases presented herein. Besides the limited availability of the specified building testbed, real-life experiments demonstrated operational effectiveness of the proposed approach in BOC applications with complex, emerging dynamics arising from the building’s occupancy and thermal characteristics. L4GPCAO outperformed the control strategy that was designed by the planers and system provider, in a conventional manner, requiring no more than five test days. [ABSTRACT FROM AUTHOR]

Bidirectional low temperature networks are a novel concept that promises more efficient heating and cooling of buildings. Early research shows theoretical benefits in terms of exergy efficiency over other technologies. Pilot projects indicate that the concept delivers good performance if heating and cooling demands are diverse. However, the operation of these networks is not yet optimized and there is no quantification of the benefits over other technologies in various scenarios. Moreover, there is a lack of understanding of how to integrate and control multiple distributed heat and cold sources in such networks. Therefore, this paper develops a control concept based on a temperature set point optimization and agent-based control which allows the modular integration of an arbitrary number of sources and consumers. Afterwards, the concept is applied to two scenarios representing neighborhoods in San Francisco and Cologne with different heating and cooling demands and boundary conditions. The performance of the system is then compared to other state-of-the-art heating and cooling solutions using dynamic simulations with Modelica. The results show that bidirectional low temperature networks without optimization produce 26% less emissions in the San Francisco scenario and 63% in the Cologne scenario in comparison to the other heating and cooling solutions. Savings of energy costs are 46% and 27%, and reductions of primary energy consumption 52% and 72%, respectively. The presented operation optimization leads to electricity use reductions of 13% and 41% when compared to networks with free-floating temperature control and the results indicate further potential for improvement. The study demonstrates the advantage of low temperature networks in different situations and introduces a control concept that is extendable for real implementation. [ABSTRACT FROM AUTHOR]

The paper studies grid-connected photovoltaic (PV)-hydrogen/battery systems. The storage component capacities and the rule-based operation strategy parameters are simultaneously optimized by the Genetic Algorithm. Three operation strategies for the hydrogen storage, namely conventional operation strategy, peak shaving strategy and hybrid operation strategy, are compared under two scenarios based on the pessimistic and optimistic costs. The results indicate that the hybrid operation strategy, which combines the conventional operation strategy and the peak shaving strategy, is advantageous in achieving higher Net Present Value (NPV) and Self Sufficiency Ratio (SSR). Hydrogen storage is further compared with battery storage. Under the pessimistic cost scenario, hydrogen storage results in poorer performance in both SSR and NPV. While under the optimistic cost scenario, hydrogen storage achieves higher NPV. Moreover, when taking into account the grid power fluctuation, hydrogen storage achieves better performance in all three optimization objectives, which are NPV, SSR and GI (Grid Indicator). [ABSTRACT FROM AUTHOR]

A two-stage robust optimization (RO) method for buildings' space heating loads (SHLs) in an integrated community energy system (ICES) is proposed. At the first stage, a bi-level optimization is deployed to formulate the hierarchical relationship between the ICES operator and consumers in buildings to enable the optimal heating pricing strategy between them. Thermal inertia of SHLs, which is modelled utilizing the Resistor-Capacitor thermal network, is used to provide heating demand response according to the optimal heating sale prices released by the ICES operator. At the second stage, the ICES operator decides the optimal energy purchase schedules from the upper energy systems after the heating sale prices are decided at the first stage. Since the day-ahead energy prices differ from the real-time ones, a RO method is adopted. The original min–max RO problem is converted into its dual problem to cooperate with the bi-level optimization. Finally, the whole optimization model is transformed into a mixed integer linear programming (MILP) based on Karush-Kuhn-Tucker conditions, duality theory, big-M method and piecewise linearization. Numerical results show that the proposed model can balance the interests between the ICES operator and consumers. And the profit of the ICES operator is 5.10% higher using RO method, compared to the deterministic optimization under energy prices uncertainties. Compared to the benchmark solution method, the computation efficiency of the final MILP model in this paper is highly improved and the convergency of the MILP model can be guaranteed. [ABSTRACT FROM AUTHOR]

Significant energy savings can be achieved in buildings by altering how occupants use and operate various building systems. The first step to successfully inducing such change requires a thorough assessment and understanding of the actual drivers and motivators of existing behaviors. The current literature on energy conservation behaviors in buildings presents significant limitations including: (1) the availability of data on human behavior in buildings; (2) the lack of consideration of various behavioral drivers (e.g., social, environmental, and economic); (3) simplified data analysis methods, which overlook combined effects of behavioral drivers; and (4) limited scopes of work to either residential or commercial buildings, overlooking potential synergies between the two. This paper aims to fill the stated gaps in the literature by proposing a comprehensive data collection and analysis framework to investigate the energy conservation motivation and actions of people in individual or groups of buildings (e.g., community or city). The framework is illustrated through a case study on a green campus located in Abu Dhabi, United Arab Emirates (UAE). Data was collected from a total of 227 campus users or residents, followed by descriptive and statistical analyses using Principal Components Analysis (PCA) and multiple linear regression models. Results indicate that energy saving awareness and motivation do not directly translate to actions, particularly at the workplace where a correlation coefficient of only 0.083 is observed. Factors such as respondents’ demographics, the level of control over building systems, and motivation drivers (e.g., financial, social, and environmental) highly affect energy saving actions and need through consideration for effective human-focused energy conservation strategies. [ABSTRACT FROM AUTHOR]

In this paper, a case study of performing a reliable statistical procedure to evaluate the quality of HVAC systems in buildings using data retrieved from an ad hoc big data web energy platform is presented. The proposed methodology based on statistical quality control (SQC) is used to analyze the real state of thermal comfort and energy efficiency of the offices of the company FRIDAMA (Spain) in a reliable way. Non-conformities or alarms, and the actual assignable causes of these out of control states are detected. The capability to meet specification requirements is also analyzed. Tools and packages implemented in the open-source R software are employed to apply the different procedures. First, this study proposes to fit ARIMA time series models to CTQ variables. Then, the application of Shewhart and EWMA control charts to the time series residuals is proposed to control and monitor thermal comfort and energy consumption in buildings. Once thermal comfort and consumption variability are estimated, the implementation of capability indexes for autocorrelated variables is proposed to calculate the degree to which standards specifications are met. According with case study results, the proposed methodology has detected real anomalies in HVAC installation, helping to detect assignable causes and to make appropriate decisions. One of the goals is to perform and describe step by step this statistical procedure in order to be replicated by practitioners in a better way. [ABSTRACT FROM AUTHOR]

Due to communication and technology developments, residential consumers are enabled to participate in Demand Response Programs (DRPs), control their consumption and decrease their cost by using Household Energy Management (HEM) systems. On the other hand, capability of energy storage systems to improve the energy efficiency causes that employing Phase Change Materials (PCM) as thermal storage systems to be widely addressed in the building applications. In this paper, an operational model of HEM system considering the incorporation of more than one type of PCM in plastering mortars (hybrid PCM) is proposed not only to minimize the customer’s cost in different DRPs but also to guaranty the habitants’ satisfaction. Moreover, the proposed model ensures the technical and economic limits of batteries and electrical appliances. Different case studies indicate that implementation of hybrid PCM in the buildings can meaningfully affect the operational pattern of HEM systems in different DRPs. The results reveal that the customer’s electricity cost can be reduced up to 48% by utilizing the proposed model. [ABSTRACT FROM AUTHOR]

The smart grid paradigm implies flexible demand and energy storage in order to cope with the variability of renewable energy sources. Buildings are often put forward as a potential supplier of flexibility services through demand side management (DSM) and distributed energy storage, partly as thermal energy. This paper presents a bottom-up approach for the quantification of this flexibility service. Cost curves are computed from the solution of optimal control problems with low-order models. These curves show the amount of flexibility and their associated cost. The method is generic and can be applied to heating, ventilation and air-conditioning (HVAC) services, thermal energy storage (TES) and local electricity production. A case study is performed on a monitored office building in Brussels, Belgium. The results reveal a large variation in both flexibility and cost depending on time, weather, utility rates, building use and comfort requirements. The study shows that for the studied day, flexibility is not for free. The mean flexibility cost has the same order of magnitude as the imbalance price in the Belgian power system. [ABSTRACT FROM AUTHOR]

The management of energy and water is increasingly gaining attention among domestic end-users. In developing countries, potable water supply is unreliable leading to alternative strategies such as rooftop water harvesting (RWH), storage and pumping. Since urban garden irrigation is the highest outdoor water consumer, optimal scheduling is important to conserve energy and water over conventional lawn irrigation methods. A model to optimally control the lawn irrigation was developed with the simulation results showing 17.4% potential water savings when using water directly from municipal sources. With RWH of a 1-h event with 1-mm rainfall from a 120-m 2 roof, the model can potentially save 23.4% of water and 73.8% in energy costs per day. Such savings are important in reducing the demand for existing water and energy sources as well as greenhouse emissions. Further, this paper looks into two strategies to maximize the pump’s life through minimizing the maintenance cost. [ABSTRACT FROM AUTHOR]

Small commercial buildings in the United States consume 47% of the total primary energy of the buildings sector. Retrofitting small and medium commercial buildings poses a huge challenge for owners because they usually lack the expertise and resources to identify and evaluate cost-effective energy retrofit strategies. This paper presents the Commercial Building Energy Saver (CBES), an energy retrofit analysis toolkit, which calculates the energy use of a building, identifies and evaluates retrofit measures in terms of energy savings, energy cost savings and payback. The CBES Toolkit includes a web app (APP) for end users and the CBES Application Programming Interface (API) for integrating CBES with other energy software tools. The toolkit provides a rich set of features including: (1) Energy Benchmarking providing an Energy Star score, (2) Load Shape Analysis to identify potential building operation improvements, (3) Preliminary Retrofit Analysis which uses a custom developed pre-simulated database and, (4) Detailed Retrofit Analysis which utilizes real-time EnergyPlus simulations. CBES includes 100 configurable energy conservation measures (ECMs) that encompass IAQ, technical performance and cost data, for assessing 7 different prototype buildings in 16 climate zones in California and 6 vintages. A case study of a small office building demonstrates the use of the toolkit for retrofit analysis. The development of CBES provides a new contribution to the field by providing a straightforward and uncomplicated decision making process for small and medium business owners, leveraging different levels of assessment dependent upon user background, preference and data availability. [ABSTRACT FROM AUTHOR]

Fuel cells are a promising technology for combined heat and power (CHP); however their efficiency and carbon benefits are often overlooked because of their reliance on fossil fuels. This paper sets out the numerical methods and evidence of real-world performance needed to compare fuel cells with other low-carbon technologies. It is demonstrated that firstly, the efficiency of present-day fuel cells is high enough to outperform the best electric heat pumps, even when these are powered exclusively by the best modern power stations. The equivalent COP (coefficient of performance) of today’s fuel cells ranges from 5 to ∞, compared to 3–4 for the best ground source heat pumps. Secondly, this high efficiency means that even when fuelled with natural gas, the heat from a fuel cell is zero- or even negative-carbon when electricity from central power stations is displaced. [ABSTRACT FROM AUTHOR]

This paper provides a review on three streams of life cycle studies that have been frequently applied to evaluate the environmental impacts of building construction with a major focus on whether they can be used for decision making. The three streams are Life Cycle Assessment (LCA), Life Cycle Energy Assessment (LCEA) and Life Cycle Carbon Emissions Assessment (LCCO 2 A). They were compared against their evaluation objectives, methodologies, and findings. Although they share similar objectives in evaluating the environmental impacts over the life cycle of building construction, they show some differences in the major focuses of evaluation and methodologies employed. Generally, it has been revealed that quite consistent results can be derived from the three streams with regard to the relative contribution of different phases of life cycle. However, discrepancies occur among the findings obtained from the three streams when different compositions of fuel mixes are used in power generation, or when the overall impacts are not contributed mostly by greenhouse gases emissions. The use of different functional units in different studies also makes it difficult to compare results with benchmarks or results from previous studies. Besides, there are drawbacks in boundary scoping, methodology framework, data inventory and practices which impair their usefulness as a decision making support tool for sustainable building designs. [ABSTRACT FROM AUTHOR]

The Energy Performance of Buildings Directive (EPBD) recast imposed on all Member States to establish representative reference buildings as an important step in drawing up a methodology for calculating cost-optimal levels of minimum energy performance requirements for buildings and building elements. In this paper, the concept of “reference buildings” and the methodologies used to characterize it are defined. A methodology for defining reference buildings in the national building stock is also proposed. This methodology consists essentially of the creation of a virtual building based on statistical data and, exceptionally, on experts’ knowledge and other sources of information. The parameters necessary for distinguishing samples of buildings and the detailed parameters for characterizing each building are defined and listed. These parameters make it possible to know the most relevant information to be drawn up for each of the buildings and how to standardize the model of data collection for all the existing reference buildings for all categories in the EU. The proposed methodology meets the needs and the existing lack of information in the definition of reference buildings in Portugal. This methodology was successfully implemented so as to define a reference building representative of the residential buildings constructed in Lisbon between 1961 and 1990. [ABSTRACT FROM AUTHOR]

• A novel Dendrite Net based on the adaptive mean square gradient is proposed. • Energy consumption prediction and analysis model of buildings is obtained based on energy consumption data of buildings. • The prediction accuracy of the proposed model is higher than most neural network methods. • The optimization direction of building envelopes is found for reducing energy consumption. • The maximum carbon dioxide emissions can be reduced by 158.17 kg. Artificial neural networks have been widely applied in construction industries. Due to dendrites of biological neurons participating in the pre-calculation of neural networks, the structure of the traditional artificial neural network needs to be adjusted subjectively. Thus, a novel dendrite net based on the adaptive mean square gradient is proposed in this paper. The energy consumption of buildings is predicted and analyzed by the proposed method to cut the carbon dioxide emissions. The adaptive mean square gradient method is used to update the weight matrix of the dendrite net method, which can avoid errors caused by selecting hidden layer nodes to improve the prediction accuracy of the proposed method. Finally, the proposed method is used to energy saving and emission reducing of the construction industry. Compared with other methods, the experimental results show the availability of the proposed method. Through predicting the heating and cooling loads based on the proposed method, the construction plan is adjusted to decrease the energy consumption of buildings. Moreover, the appliances energy consumption is predicted and analyzed by the proposed method to improve energy efficiency and cut carbon dioxide emissions. [ABSTRACT FROM AUTHOR]

• Temporal variation of environmental impacts related to buildings electricity consumption is modelled. • Short term (e.g. peak demand) and long term aspects (e.g. variation of production capacities) are accounted for. • The method was applied in a case study, addressing uncertainties. • Consequences of electrification in the building sector can be evaluated. • Electrification does not necessarily reduce greenhouse gases emissions. The building stock is a major contributor to energy consumption and greenhouse gases emissions (GHG), which can be evaluated using life cycle assessment (LCA). Electrification of buildings, e.g. replacing fuel and gas boilers with heat pumps, in order to reduce these emissions is often seen as an option, but this will have short term effects by increasing peak demand, and long term effects by requiring more electricity production capacities. In this paper, a methodology to account for such interaction in LCA is presented. It connects three models addressing: market allocation on a national scale over a long term period, short term variation (i.e. seasonal, daily and hourly) of the electricity mix also on a national scale, and building energy simulation at the scale of one building. This methodology has been applied to a case study including a sample of buildings in the French context, but it can be used in other countries. Six buildings have been studied over 100 years considering 50 energy transition scenarios. Results show that the environmental impacts vary more depending on the scenarios than on the types of the building. Marginal mixes considered in consequential LCA are mainly composed of coal, gas, nuclear and peak technology production which explains the highest values of the different impacts compared to average mixes used in attributional LCA. This approach allows to address uncertainties related to electricity production. [ABSTRACT FROM AUTHOR]

• Background is provided about the need for tools to support regional energy baselining and efficiency planning. • 296 homes in Pennsylvania are simulated in EnergyPlus using a batch simulation tool and energy audit records. • Simulation results are scaled and compared to building level electricity use data and utility sales data. • Analysis reveals discrepancies between simulated and actual energy use that varies by utility. • The role of building simulation tools for energy baselining and efficiency planning is discussed. Building simulation tools are seeing growing use in the residential sector due to a combination of increased interest in building energy performance, more user-friendly software packages, and decreasing computing power requirements. These tools are now being applied in batch simulations, where large numbers of homes are simulated at once to understand existing building stock energy consumption and potential energy efficiency improvements to make to those stocks. As these types of applications grow, attention is needed to ensure that simulation inputs and outputs are handled with an appropriate understanding of the uncertainties involved. In this paper, we use a batch simulation tool to translate energy audit records of 296 existing single-family homes in Pennsylvania into EnergyPlus building simulation models. We compare the results of these simulations to actual metered electricity use data from these homes, representative device-level data, and local utility data to better understand the abilities and limits of such modeling practices. Results show that while batch simulations are capable of producing accurate estimates of the energy consumption of a large number of homes, many more homes had their energy consumption overestimated than underestimated, resulting in a significant net overestimate for the sample as a whole. When results are scaled up to the utility level, simulation accuracy varies by utility with net overestimates ranging from around 15% to 25%. Comparisons of individual simulation results to metered consumption identify electric space heating systems as the source of much of this error. This highlights the importance of gathering accurate, detailed information on these systems to generate reliable simulation results. NREL has recently released a batch simulation tool called ResStock that takes a more statistically robust approach to simulating representative archetype homes as opposed to actual existing homes. The results of this study suggest that the type of probabilistic treatment of building characteristics underlying that tool will provide more meaningful results than trying to simulate actual buildings. With additional validation measures similar to those used in this study, ResStock can likely be used to reliably estimate baseline energy consumption and quantify efficiency savings opportunities and can serve as a valuable decision-making tool for state, local, and utility efficiency program designers. [ABSTRACT FROM AUTHOR]