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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]

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]

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]

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]

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]

• 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]