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The concept of ‘Buildings as Power Stations’ (BAPS) represents a major shift in the way that electricity is generated, stored and used. Buildings are no longer simply consumers of electricity, but active players in the electric power system. Reducing energy demand to ‘PassivHaus’ levels of performance and the full integration of photovoltaic modules and wind turbines with buildings is itself a challenge to architects and house builders. Combining these with the sizing of the batteries for electrical storage through a ‘systems’ approach, optimizing performance and cost across reduced energy demand, renewable supply and storage, needs a suitable ‘user-friendly’ modeling framework, which is currently not generally available to designers. The new BAPS tool presented in this paper assesses if a building has the potential to become a ‘Power Station’ by analyzing the energy generated from building-integrated renewables (including, solar and wind), the effect of using electrical energy storage systems and the impact of introducing demand reduction technologies. The BAPS tool enables an architect, via an easy to understand user interface, to assess the potential for renewable energies and battery storage and provides guidance on the selection of the battery system for a range of residential applications. The paper describes how the tool has been used in the design of the prototype ‘near zero’ carbon SOLCER House, currently under construction in South Wales, UK. The tool has been used to evaluate the building energy system, including the sizing of solar PV panels and the li-ion battery storage, in the context of the buildings reduced demand for electricity, through the use of LED lighting and high efficiency appliances. The optimum mix of renewable energy and grid based energy supply is presented for the SOLCER House. [ABSTRACT FROM AUTHOR]

More than two thirds of the U.S. electricity consumption occurs in buildings, which also are responsible for about 40% of the world's CO2 emissions. Therefore, the “green” building movement has become widespread in an effort to reduce buildings’ impact on the resource use and the environment. The USGBC's LEED rating system in one such effort intended to recognize high performance buildings. One of the USGBC's objectives is to reduce building energy consumption, and this paper aims to test this energy-reduction hypothesis. The methodology includes investigating LEED-certified buildings and comparable non-certified buildings on a university campus, and measuring differences in their energy performance. Heating, cooling, and electricity data was collected from all campus buildings and their energy unit intensity (EUI) was calculated. The preliminary analysis shows LEED buildings are not displaying major differences in energy performance when compared to their non-LEED counterparts. This paper's findings make the case for more performance-based certifications, similar to the LEED O&M certification, which should be awarded based on actual energy performance of facilities as opposed to intended performance based on design data. [ABSTRACT FROM AUTHOR]

This paper investigates factors associated with variation in daily total (electricity and gas) energy consumption in domestic buildings using linked pre-COVID-19 smart meter, weather, building thermal characteristics, and socio-technical survey data covering appliance ownership, demographics, behaviours, and attitudes for two nested sub-samples of 1418 and 682 British households selected from the Smart Energy Research Laboratory (SERL) Observatory panel. Linear mixed effects modelling resulted in adjusted R2 between 63% and 80% depending on sample size and combinations of contextual data used. Increased daily energy consumption was significantly associated (p -value < 0.05, VIF < 5) with: households living in buildings with more rooms and bedrooms, that are older, more detached, have air-conditioning, and experience colder (more heating degree days) or less sunny weather; households with more adult occupants, more children, older adult occupants, higher heating temperature setpoints, and that do not try to save energy. The results demonstrate the value of smart meter data linked with contextual data for improving understanding of energy demand in British housing. Accredited UK researchers are invited to apply to access the data, which has recently been updated to include over 13,000 households from across Great Britain. This paper provides guidance on appropriate methods to use when analysing the data. [ABSTRACT FROM AUTHOR]

In most countries, electricity is charged depending on the time of use, which usually includes two tariffs: the low tariff, during night time, and the high tariff, during daytime. On top of their energy payment, large electricity consumers pay a monthly fee per kW of peak demand. Therefore, installation of stationary battery storage can reduce electricity payments for large consumers in two ways: by reducing the peak demand and by shifting consumption from the high tariff to the low tariff. The aim of this paper is to formulate a model to determine optimal energy and power capacity of a stationary battery storage in order to minimize electricity payments. Since the future electricity consumption is uncertain, after formulating the deterministic model, two additional models that consider uncertainty are introduced. The first one is the stochastic model, which considers uncertain scenarios, and the second one is the robust model, where uncertainty is represented by only an uncertainty set, without an assumption on the distribution of uncertainty within this set. The proposed models are tested and compared on a real-world load data of a hotel in Croatia. The case study indicates that deterministic and stochastic formulations result in slightly better investment decisions than the robust formulation. [ABSTRACT FROM AUTHOR]

In the US, building sector consumes approximately 41% of all U.S. primary energy and 70% of all generated electricity. Office building uses the largest percentage of primary and derived energy in the commercial buildings sector. Therefore, energy saving is the primary target of building renovation and equipment retrofit. To estimate energy savings after a renovation or equipment retrofit, measurement and verification (M&V) must be conducted based on energy use models or benchmark tools to track and assess savings. Although some methods and modeling tools are applied more often, there is no well-accepted model formulation methodology and some methods have high uncertainty. This paper discusses a method to formulate the energy use model as an inverse regression model via an advanced and robust high-dimensional data analysis method. According to the inverse regression model, independent variables determined to be most influential to the energy consumption should be metered prior to building retrofit or audit. To select the most influential variables, this research considers a comprehensive set of potential key variables to avoid underspecified model issue. The models established by this method are observed to have great power in predicting the building energy consumption. More importantly, through the variable selection procedure, the method identifies key variables that should be monitored to continuously improve the building energy performance. A simulation study and a case study are presented to show the effectiveness of the new approach. [ABSTRACT FROM AUTHOR]

This paper presents a building stock energy model for the estimation of hourly electricity consumption for a large group of residential buildings. A Monte Carlo model stochastically generates a large sample of dwellings representative of the building stock and the correspondent number of user profiles, statistically supported by a web survey about the use of energy in dwellings for space heating and cooling. The model uses hourly energy balance equations to estimate energy needs and calculates the mean annual electricity consumption for regularly occupied dwellings with an error below 3%. Model is also validated against independent smart-metered data of about 250 dwellings. Hourly electricity consumption results feature an overall normalised mean absolute error of 11% and normalised root mean square error of 16%. The maximum relative difference is  ± 72% and the maximum absolute error is ≃217 Wh/h. The model is considered to be able to predict hourly electricity consumption accurately. [ABSTRACT FROM AUTHOR]

Passive Houses are buildings with low energy demand for heating (below 15 kW h/m 2 year), but provide comfortable indoor conditions. This paper presents the performance of an energy system designed for such a house located in Bucharest. Building system combines the earth-air heat exchange (EAHX) technology with photovoltaic panels. The photovoltaic panels were analyzed in terms of energy production and economic investment. The energy demand of the house was simulated using the EnergyPlus software in order to understand the house performance during cold and hot seasons using various occupancy scenarios. The results showed that the energy demand for a passive house is 14 kW h/m 2 year. Finally, the actual data measurement indicated that the passive house, has a good thermal comfort with a minimum energy consumption close to 13 kW h/m 2 year. The electricity generated from PV system during one year was 1556.5 kW h/year, the rest of necessary energy for consumption being supplied by the grid. [ABSTRACT FROM AUTHOR]

The objective of this paper is to discover the emergent energy performance and determinants of energy waste in buildings. Electricity consumption in the U.S. attributes to 73% of energy waste in buildings and much of this waste is due to improper design, operation, and use of appliances. In particular, the operation or use phase of buildings and the way occupants behave significantly contribute to energy waste. Understanding the determinants of energy waste during the operation phase of buildings is a challenging task due to the complex interactions between the occupants, building units, and appliances. To decode these complex interactions and facilitate a better understanding of the determinants of energy waste, a simulation approach is used in this study. An agent-based simulation model was developed to capture the diverse attributes and dynamic behaviors of building occupants at the interface of human-building-appliance interactions. The application of the proposed model is demonstrated in a case study. Using simulation experiments, the interactions between occupant, building unit and appliance on energy consumption were investigated. The simulation model also was used for estimating determinants of energy waste. In addition, the simulation model includes a visualization interface that facilitates communication of strategies between the buildings users and facility managers. The results will highlight the significant attributes and effective strategies for energy waste reduction at the interface of human-building-appliance interactions. This information has potentially significant implications for building designers, facility managers, and users through a better understanding of emergent energy performance of buildings. [ABSTRACT FROM AUTHOR]

Even though industry consumes nearly half of total energy production, the relative share of total energy consumption related to heating and operating buildings is growing constantly. The motivation for this study was to reveal the differences in electricity use and district heating consumption in school buildings of various ages during the working day and also during the night when human-based consumption is low. The overall aim of this study is to compare the energy (electricity and heating) consumption of six school buildings in Kuopio, Eastern Finland. The selected school buildings were built in different decades, and their ventilation and building automation systems are also inconsistent. The hourly energy consumption data was received from Kuopion Energia, the local energy supply company. In this paper, the results of data analysis on the energy consumption in these school buildings are presented. Preliminary results show that, generally speaking, new school buildings are more energy-efficient than older ones. However, concerning energy efficiency, two very new schools were exceptional because ventilation was on day and night in order to dry the building materials in the constructions. The novelty of this study is that it makes use of hourly smart metering consumption data on electricity and district heating, using modern computational methods to analyse complex multivariate data in order to increase knowledge of the buildings’ consumption profiles and energy efficiency. [ABSTRACT FROM AUTHOR]

Using a visualization engine to analyze power meter generated data about energy consumption of a building can provide immediate and informative feedback for energy conservation research. This paper proposed a platform, real-time replay system (RTRS), to provide such a visualized and interactive environment through the Unity 3d game engine. RTRS combines two data sources: Revit, a building information model (BIM) tool, and WiFi power socket sensors into Unity 3d and replays electricity usage events. RTRS users can easily change the scene or jump to the given time frame identified as a wasteful operation, and the system will display potential energy conservation suggestions. In addition, RTRS users can set up event triggering parameters or query conditions against the sensor database inside RTRS. In overall, RTRS can provide an intuitive and easy-to-use platform for managing electricity usage data of a building, reducing the time and efforts for looking up information at the raw data level. [ABSTRACT FROM AUTHOR]

In this paper, the development of a general framework for building level power demand forecasting and its applications to supervisory control and demand management are presented. Models of thermal loads, while rigorous and insightful, do not directly extrapolate to measures of power consumption and cannot be easily applied to a variety of buildings. Ultimately, building operators are interested in managing power consumption as energy costs and opportunities are directly related to the power variable. Our work develops Auto-Regressive models with eXogeneous inputs (ARX) to forecast power demand in conjunction with existing physics based modeling approaches and enhances the current control framework for building energy management. The main contributions of this work are identifying and incorporating building level measurements as inputs, and evaluating the use of power forecast models for supervisory control and demand response (DR). The move towards a smarter grid is expected to provide extensive data on building conditions and power consumption, which we can include in the model development. Options for model inputs and outputs are investigated depending on possible measurements, and their effect (or sensitivity) on the modeling and decision making processes are evaluated. It is shown that an appropriate selection of exogenous inputs related to the control action is necessary to capture the effect of common demand management practices such as precooling. The forecasting capabilities are also demonstrated on a simplified building model and on data collected from a real building. [ABSTRACT FROM AUTHOR]

The electric grid is evolving. Smart grids and demand response systems will increase the performance of the grid in terms of cost efficiency, resilience and safety. Accurate load forecasting is an important issue in the daily operation and control of a power system. A suitable short term load forecasting will enable a utility provider to plan the resources and also to take control measures to balance the supply and demand of electricity. The aim of this paper is to create a method to forecast the electric load in a non-residential building. Another goal is to analyse what kind of data, as weather, indoor ambient, calendar and building occupancy, is the most relevant in building load forecasting. A simple method, tested with three different models, such as MLR, MLP and SVR, is proposed. The results, from a real case study in the University of Girona, show that the proposed forecast method has high accuracy and low computational cost. [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]

• End-use of electricity consumption in residential buildings in Gulf Cooperation Council countries was presented. • Discrepancies in certain indicators have been observed by the end-use of electricity consumption. • Improving the efficiency of air conditioners and thermal insulation of the walls are the best measures to conserve electricity in residential buildings. • The results can be used in areas with a similar climate, living standard, buildings types, and the inhabitants' behavior. Energy conservation and improvement in energy efficiency yield multiple technical, economic, environmental, and social benefits and realize comfort and sustainability of residential buildings (RBs). This review paper presents the end-use of electricity consumption in RBs and the results of economic analysis. In addition, a description and discussion of studies on energy conservation measures, Net Zero Energy Building, and renewable energy in RBs are discussed. The percentage of end-used energy consumption for different purposes obtained in many studies are often not consistent. Moreover, discrepancies in certain indicators have been observed, necessitating the need to clarify the factors in the consumption of electricity in RBs. Further, a few studies used the term energy consumption in buildings (total or electrical); however, specifying the type of buildings to identify and use the necessary indicators of energy consumption in each type of building are desirable. Furthermore, most studies emphasized improving the efficiency of air conditioners and thermal insulation of the walls as these are the best measures to conserve electricity. [ABSTRACT FROM AUTHOR]