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Highlights • Redefine and classified "building energy performance gap". • Identify the important role of life-cycle and stakeholders. • Identify, classify and discuss the root causes of "building energy performance gap". • Discuss the corresponding strategies to close the gap. • Identify eight areas and topics requiring future research. Abstract The reliability, security, and sustainability of energy generation and supply are of global importance and the building sector accounts for up to 32% of total energy consumption, which makes it a key player in the domain. Previous research has identified that the actual energy consumption in buildings could be as much as 2.5 times of the predicted or simulated. This large building energy performance gap (BEPG) between the predicted and actual consumption has caused a significant problem for building energy supply and demand management and therefore have attracted increasing attention from researchers around the world. These researches have resulted in a large number of publications over the last decade. However, research has not reached the phase where the root causes of BEPG could be effectively identified, managed, and eliminated. There remains a lack of systematic and comprehensive review of the current literature to understand the current state of play, and set directions for future research. To fill this gap, in this paper, a thorough survey and review of BEPG research was carried out. The paper collected and analyzed 227 relevant publications and developed a framework for better understanding previous BEPG research. Subsequently, an in-depth analysis of BEPG research was conducted to explore its causes and corresponding strategies (including design concept, "hard" technologies and "soft" measures). Not only the interaction among causes but also the strengths and limitations of corresponding strategies were discussed in detail. Future research is finally recommended based on the limitations of exist strategies, including (1) building energy performance life-cycle thinking; (2) energy performance information integrity; (3) big data collection and analytical method; (4) stakeholders' attributions, decision criteria and behavior; (5) stakeholder interactions; (6) modelling and simulation validation; (7) multidisciplinary approach; and (8) building system flexibility. [ABSTRACT FROM AUTHOR]

A simple way to reduce energy consumption is to minimize heating use during unoccupied periods. This implies the possibility of adjusting the room temperature setpoint. However, systems with a large thermal capacity cannot follow sudden setpoint changes because of their thermal inertia. A model predictive control (MPC) allows the harnessing of this inertia in order to reduce heating costs and improve comfort. This advanced control technique is based on disturbances anticipation (occupation, weather conditions) and requires a model of the system which has to be controlled. Therefore, the use of such controller needs a reliable model that describes well the dynamics of the room on upcoming days. This paper presents a method for the selection of the model (type, level of complexity) to be implemented in a MPC controller to anticipate the control of a long time response floor heating system on a real building. The demonstration room and embedded systems serving as experimental support are presented. Short measurement periods are carried out to identify the model parameter values minimizing the gap between model output and measurement. Gray-box models based on electrical analogy and state-space representation are proposed. They are constructed from physical knowledge and then identified by choosing the most appropriate measurement series. A sensitivity analysis method (Morris) is used to improve the quality of the identified model which satisfies control criteria with two specific validation measurement series. In a complementary paper, the predictive controller integrating the selected model is compared to more conventional management strategies in simulation and on-site with the experimental building. [ABSTRACT FROM AUTHOR]

The use of radiant technology in attics aims to reduce the radiation component of heat transfer between the attic floor and roof decks, gables, and eaves. Recently, it has been shown that EnergyPlus underestimates the savings using radiant technologies in attic spaces. The aim of this study is to understand why EnergyPlus underestimates the performance of radiant technologies and provide a solution strategy that works within the current capabilities of EnergyPlus. The analysis uses three attic energy models as a baseline for comparison for EnergyPlus. Potential reasons for the discrepancies between the attic specific energy models and EnergyPlus are isolated and individually tested. A solution strategy is proposed using the Energy Management System (EMS) capabilities within EnergyPlus. This solution strategy produces similar results to the other attic specific energy models. This paper shows that the current capabilities of EnergyPlus are sufficient to simulate radiant technologies in attics. The methodology showcased in this paper serves as a guide for engineers and researchers who would like to predict the performance radiant technology in attics using the whole building energy software, EnergyPlus. [ABSTRACT FROM AUTHOR]

This paper provides a study of the optimal scheduling of building operation to minimize its energy cost under building operation uncertainties. Opposed to the usual way that describes thermal comfort using a static range of air temperature, the optimization of a tradeoff between energy cost and thermal comfort predicted mean vote (PMV) index is addressed in this paper. In order to integrate the calculation of the PMV index with the optimization procedure, we develop a sufficiently accurate approximation of the original PMV model which is computationally efficient. We develop a model-based periodic event-triggered mechanism (ETM) to handle the uncertainties in the building operation. Upon the triggering of predefined events, the ETM determines whether the optimal strategy should be recalculated. In this way, the communication and computational resources required can be significantly reduced. Numerical results show that the ETM method is robust with respect to the uncertainties in prediction errors and results in a reduction of more than 60% in computation without perceivable degradation in system performance as compared to a typical closed-loop model predictive control. [ABSTRACT FROM AUTHOR]

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO 2 emissions. The drive to reduce energy consumption and associated greenhouse gas emissions from buildings has acted as a catalyst in the increasing installation of meters and sensors for monitoring energy use and indoor environmental conditions in buildings. This paper reviews the state-of-the-art in building energy metering and environmental monitoring, including their social, economic, environmental and legislative drivers. The integration of meters and sensors with existing building energy management systems (BEMS) is critically appraised, especially with regard to communication technologies and protocols such as ModBus, M-Bus, Ethernet, Cellular, ZigBee, WiFi and BACnet. Findings suggest that energy metering is covered in existing policies and regulations in only a handful of countries. Most of the legislations and policies on energy metering in Europe are in response to the Energy Performance of Buildings Directive (EPBD), 2002/91/EC. However, recent developments in policy are pointing towards more stringent metering requirements in future, moving away from voluntary to mandatory compliance. With regards to metering equipment, significant developments have been made in the recent past on miniaturisation, accuracy, robustness, data storage, ability to connect using multiple communication protocols, and the integration with BEMS and the Cloud – resulting in a range of available solutions, selection of which can be challenging. Developments in communication technologies, in particular in low-power wireless such as ZigBee and Bluetooth LE (BLE), are enabling cost-effective machine to machine (M2M) and internet of things (IoT) implementation of sensor networks. Privacy and data protection, however, remain a concern for data aggregators and end-users. The standardization of network protocols and device functionalities remains an active area of research and development, especially due to the prevalence of many protocols in the BEMS industry. Available solutions often lack interoperability between hardware and software systems, resulting in vendor lock-in. The paper provides a comprehensive understanding of available technologies for energy metering and environmental monitoring; their drivers, advantages and limitations; factors affecting their selection and future directions of research and development – for use a reference, as well as for generating further interest in this expanding research area. [ABSTRACT FROM AUTHOR]

• The data requirements of quota methods are summarized, and a city-scale database containing 670 credible government office buildings is established. • By comparing the processes, the results and applicability, the selection basis of quota methods under different conditions is refined. • A flow chart of the formulation process and method selection of city-level building energy consumption quotas is constructed. • It is proposed that special attention should be paid to the outliers, and the characteristics of outliers in Chongqing are summarized. Energy consumption quota policies are effective management measures to promote energy conservation and emission reduction in China. Cities need to choose an appropriate method to formulate local energy consumption quota standards. This paper is based on the data of 670 government office buildings in Chongqing and the selected quantile method (QM), quota-level method (QLM) and multiple linear regression (MLR) method, which are widely used in the formulation of city-level energy quotas. By comparing their interpretability, evaluation, consistency and stability, the application conditions and applicability of each method are summarized. MLR has the strongest explanatory power but the highest requirements for the technology and information dimensions. The QLM is more explanatory and stable than the QM and is more suitable for the initial stage of energy quota management. When using the QLM, we should consider scientific classifications and reasonable quota levels. Special attention should be given to "outliers" with abnormal evaluations of the quota results. The outliers in Chongqing are characterized as buildings that have been used for more than 10 years and have an area of less than 5000 m2. Finally, this paper constructed a flow chart of the formulation process and method selection to help policymakers better understand how to choose the most appropriate method and identify inefficient buildings. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an energy management strategy for a commercial building in supermarket application. Some objectives are established as load shedding, to reduce the electricity bill and the CO2 emissions of commercial building, using photovoltaic (PV) and storage systems. An energy management supervision strategy based on the rules of the electricity bill will be presented. This paper focuses on the supervision strategy with the help of fuzzy logic and a graphical methodology to build it. It is shown, with the help of simulations and some economic and ecological indicators that the energy bill cost and the CO2 emissions can be reduced by using the proposed solution. [Copyright &y& Elsevier]

Abstract: This paper presents the key factors methodology that supports energy managers in determining the optimal building operation strategy in relation to both energy consumption and thermal comfort. The methodology is supported by the utilization of calibrated building energy simulation models that match measured data gathered by an extensive measurement framework. Building management systems, do not allow energy managers to test the impact of changes in control settings of energy systems on energy consumption and thermal comfort. The proposed methodology and prototype tool chain enables energy managers to virtually develop and test the impact of proposed changes to the control settings in the building energy systems prior to their implementation in the physical building. The paper outlines the proposed methodology defining the underpinning concepts and illustrating the performance metrics required to capture the effect of different building operation strategies. A case study is discussed to demonstrate the application of the methodology. [Copyright &y& Elsevier]

Abstract: This paper develops an energy management platform for intelligent buildings using a SCADA system (Supervisory Control and Data Acquisition). This SCADA system integrates different types of information coming from the several technologies present in modern buildings (control of ventilation, temperature, illumination, etc.). The developed control strategy implements an hierarchical cascade controller where inner loops are performed by local PLC (Programmable Logic Controller), and the outer loop is managed by a centralized SCADA system, which interacts with the entire local PLC network. In this paper a predictive controller is implemented above the centralized SCADA platform. Tests applied to the control of temperature and luminosity in huge-area rooms are presented. The developed predictive controller optimizes the satisfaction of user explicit preferences coming from several distributed user-interfaces, subjected to the overall constraints of energy waste minimization. In order to run the predictive controller with the SCADA platform a communication channel was developed to allow communication between the SCADA system and the Matlab application where the predictive controller runs. [Copyright &y& Elsevier]

Buildings consume a significant amount of energy worldwide in maintaining comfort for occupants. Building energy management systems (BEMS) are employed to ensure that the energy consumed is used efficiently. However these systems often do not adequately perform in minimising energy use. This is due to a number of reasons, including poor configuration or a lack of information such as being able to anticipate changes in weather conditions. We are now at the stage that building behaviour can be simulated, whereby computer programs can be used to predict building conditions, and therefore enable buildings to use energy more efficiently, when integrated with BEMS (i.e. simulation assisted control [1]). In this paper we demonstrate a low cost BEMS that uses building simulation to predict optimised electrical heating startup control points. Those that use electricity for heating in Scotland, where this study was based, tend to be fuel-poor, hence there is a strong case for optimisation, particularly when electricity costs nearly three times as much as the equivalent unit of gas for heating applications. The proposed system demonstrated a 50% energy saving in the reduced heating time compared to scheduling when retrospectively evaluated. [ABSTRACT FROM AUTHOR]

In Germany, policy-induced energy efficiency improvements typically aim at reducing primary energy consumption. Private households, on the contrary, pursue the maximization of wellbeing or, in microeconomics jargon, the maximization of utility of the occupants. There is a marked difference between upfront-calculated energy performance ratings (EPRs) and actual heat energy consumption (HEC). From an energy and environmental policy point of view, deviations of energy consumption from ex ante calculated EPRs is problematic, as it jeopardizes their usefulness for (prospective) homeowners, policy-makers and researchers relying on EPRs as a guidance. The EPR-HEC gaps reported are, apart from deviations from the mean aggregate values (i.e. heterogeneity), often attributed to anticipated or unanticipated behavioral effects. From an energy economist's point of view, energy rebound induced by lowered costs per unit of energy service due to increased energy efficiency is one explanation. The related literature almost entirely treats building-specific EPRs as universal standards, trying to explain the empirically observed discrepancies. In this paper, we investigate to what extent the current EPR scheme in place in Germany today can address behavioral issues. To this end, based on two different multi-family housing data sets for Germany and regression analysis, we empirically investigate the deviations between EPRs used in the regulatory norms and the HEC levels observed in reality. We find evidence that it is not necessarily the behavioral dimension, but rather the static and mostly technically guided calculation of the EPRs itself that accounts for the major part of the deviations. We conclude that there is indeed a need for further improvements in the field of EPR regulation and methodology. [ABSTRACT FROM AUTHOR]

Virtual in-situ sensor calibration (VIC), using Bayesian MCMC with system models, can be applied to large-scale building sensor networks in order to calibrate multiple working sensors including virtual and physical sensors. Using the calibration strategies herein, VIC methods work to overcome various systematic errors (from simple to troublesome) that cannot otherwise be handled by a conventional calibration. Thus, it is important to understand and evaluate the VIC strategies in a whole system with various error conditions to ensure reliable performance of VIC in the operational stage. This study applied individual calibration strategies to a whole building system (a LiBr–H 2 O refrigeration system), and their effectiveness and limitations in solving the VIC problem were evaluated for the calibration of each type of working sensor under the various error conditions that cause negative effects. The combined strategies are also suggested to improve calibration accuracy by overcoming the individual limitations. This paper (1) explains how to formulate the individual and combined VIC strategies with Bayesian MCMC, (2) shows their capability to handle negative effects and limitations, and (3) demonstrates how combined strategies surmount the individual limitations for a high accuracy in various error conditions. [ABSTRACT FROM AUTHOR]

Assessment of the energy associated with the material production, transportation and construction of buildings provides an opportunity for reducing the use of energy and improving sustainability. Building Information Modeling (BIM) provides a platform to incorporate sustainability information in the design of buildings. However, interoperability of BIM with Life Cycle Assessment (LCA) tools needs further investigation. Previous research in this area has either partially employed BIM; data was exported from the main BIM authoring tool and then auxiliary tools were utilized to evaluate the model, thereby causing a disconnect between the model and analysis resulting in non-interoperable systems, or has ignored the importance of retaining the LCA results within the BIM environment. To address this issue, this paper presents a framework to estimate the embodied energy content within the native BIM environment. The implementation of this framework is illustrated by the development of a tool for estimation of material embodied energy, transportation energy and construction energy. A prototype of the tool has been implemented on a case project to establish the workability of the framework. [ABSTRACT FROM AUTHOR]

In situ measurements of low thermal transmittance façades are required to ensure compliance with energy performance strategies for new nearly zero-energy buildings (nZEB) and with energy policies for the transition of existing building stock to nZEB. The aim of this paper was to enhance the accuracy of the in situ measurement of low U-value façades, employing the widely used ISO 9869-1:2014 HFM method and exploring the limits of its conditions. To refine the testing conditions, three variables were analysed and compared with indications of ISO 9869-1:2014 and the existing literature: the temperature difference, the test duration and the accuracy of equipment. A continuous experimental campaign was conducted in a building mock-up. The findings showed that to accurately measure in situ low U-value façades, the temperature differences must be greater than those indicated in the existing literature. Temperature differences above 19 °C required a test duration of 72 h, while for lower temperature differences the test duration must be prolonged. The accuracy of temperature sensors had a greater impact on the accuracy of measurement in the initial cycles of the test. Likewise, the accuracy of ambient temperature sensors was found to have a considerable influence on the uncertainty of measurements. [ABSTRACT FROM AUTHOR]

Good models for building thermal behaviour are an important part of developing building energy management systems that are capable of reducing energy consumption for space heating through model predictive control. A popular approach to modelling the temperature variations of buildings is grey-box models based on lumped parameter thermal networks. By creating simplified models and calibrating their parameters from measurement data, the resulting model is both accurate and shows good generalisation capabilities. Often, parameters of such models are assumed to be a combination of different physical attributes of the building, hence they have some physical interpretation. In this paper, we investigate the dispersion of parameter estimates by use of randomisation. We show that there is significant dispersion in the parameter estimates when using randomised initial conditions for a numerical optimisation algorithm. Further, we claim that in order to assign a physical interpretation to grey-box model parameters, we require the estimated parameters to converge independently of the initial conditions and different datasets. Despite the dispersion of estimated parameters, the prediction capability of calibrated grey-box models is demonstrated by validating the models on independent data. This shows that the models are usable in a model predictive control system. [ABSTRACT FROM AUTHOR]

The thermo-hygrometric treatment related to the air change in buildings requires a relevant quota of the total energy demand of HVAC systems, especially when the ventilation demand is significant. A correct energy saving strategy therefore should always focus on the use of suitable techniques to reduce this energy consumption. As proved by the modern theories on comfort, less strict values can be accepted for the internal humidity set points without compromising indoor comfort conditions. In addition, Demand Controlled Ventilation (DCV) gives the opportunity to reduce energy requirements. This paper investigates the opportunities offered by the installation of a DCV system and a Building Management System (BMS) able to perform long term monitoring of the HVAC system in a real case study. This refers to a historic building in Venice in the area of the harbour and recently transformed into a modern university facility. Starting from the experimental data recorded by BMS, an optimization of the control strategies and simple tuning actions of the DCV controller were possible. Results validate the use of more flexible set points of indoor relative humidity and long term on-line tuning even in absence of self-learning control systems, as well as they highlight the achievement of remarkable energy savings thanks to these actions. The research demonstrates the fundamental contribution of successive control performance assessments by long term monitoring to individuate possible weaknesses in DCV system management. [ABSTRACT FROM AUTHOR]

Built-up areas tend to comprise a variety of buildings with diverse and complex shapes, functions and construction characteristics. This variety is the source of significant challenges when calculating building energy use at the building stock level. Moreover, the process of developing stock models usually requires large amounts of data that are frequently scarce, nonexistent or at least not publicly available. Under these circumstances, defining a limited set of reference buildings representing the stock is useful to study the actual energy consumption and the potential effects of different energy conservation measures. This paper presents a new method for developing typical residential reference buildings at district level for bottom-up energy modeling purposes. By means of widely and freely available satellite images, an information database of building shapes is created and a clustering analysis of the geometrical features is performed to define a number of archetypes representative of the heating and cooling energy demand of the district. The method is tested and demonstrated through the case study of the Yuzhong District in Chongqing (China) by comparing the Energy Use Intensity (EUI) of the archetypes derived in this way against detailed dynamic simulations. Results show very small differences in the estimated stock energy consumption (+0.03% in heating energy consumption and +2.97% in cooling energy consumption). [ABSTRACT FROM AUTHOR]

Modern district energy systems are highly complex with several controllable and uncontrollable variables. To effectively manage a multi-vector district requires a holistic perspective in terms of both modelling and optimisation. Current district optimisation strategies found in the literature often consider very simple models for energy generation and conversion technologies. To improve upon the state of the art, more realistic and accurate models must be produced whilst remaining computationally and mathematically simple enough to calculate within short periods. Therefore, this paper provides a comprehensive review of modelling techniques for common district energy conversion technologies including Power-to-Gas. In addition, dynamic building modelling techniques are reviewed, as buildings must be considered active and flexible participants in a district energy system. In both cases, a specific focus is placed on artificial intelligence-based models suitable for implementation in the real-time operational optimisation of multi-vector systems. Future research directions identified from this review include the need to integrate simplified models of energy conversion units, energy distribution networks, dynamic building models and energy storage into a holistic district optimisation framework. Finally, a future district energy management solution is proposed. It leverages semantic modelling to allow interoperability of heterogeneous data sources to provide added value inferencing from contextually enriched information. [ABSTRACT FROM AUTHOR]

This paper proposes a novel Case Based Reasoning (CBR) application for intelligent management of energy resources in residential buildings. The proposed CBR approach enables analyzing the history of previous cases of energy reduction in buildings, and using them to provide a suggestion on the ideal level of energy reduction that should be applied in the consumption of houses. The innovations of the proposed CBR model are the application of the k -Nearest Neighbors algorithm (k-NN) clustering algorithm to identify similar past cases, the adaptation of Particle Swarm Optimization (PSO) meta-heuristic optimization method to optimize the choice of the variables that characterize each case, and the development of expert systems to adapt and refine the final solution. A case study is presented, which considers a knowledge base containing a set of scenarios obtained from the consumption of a residential building. In order to provide a response for a new case, the proposed CBR application selects the most similar cases and elaborates a response, which is provided to the SCADA House Intelligent Management (SHIM) system as input data. SHIM uses this specification to determine the loads that should be reduced in order to fulfill the reduction suggested by the CBR approach. Results show that the proposed approach is capable of suggesting the most adequate levels of reduction for the considered house, without compromising the comfort of the users. [ABSTRACT FROM AUTHOR]

Ubiquitous modernization of building energy and management systems brings tendencies for an effective integration of renewables in buildings. This article shows how solar energy can be in charge of energy savings within a building by integration of solar systems with Trombe wall. Numerical calculations of Trombe wall thermal resistance and its performances in comparison with a common wall are considered. Additionally, energy-efficiency gaining by a combination of PV modules and Trombe wall, using simulations by SAM (System Advisor Model) software is discussed. PV module parameters and their impact on its electricity production are observed and analyzed. The PV module parameters, analyzed in the paper are: the tilt angle α of PV modules, incident angle β of direct sunrays on PV modules, normal beam irradiation N and PV cells operating temperature. With bigger incident angle β of direct sunlight on PV modules, the normal beam irradiation is smaller, which brings less electricity production. The impact of PV module operating temperature can be neglected in comparison with the impact of tilt angle on PV module electricity production. The optimal tilt angle of PV modules for generating maximum annual electricity is around latitude angle. It varies with normal beam irradiation to achieve the maximum monthly electricity production. [ABSTRACT FROM AUTHOR]

• An overall panorama of the school buildings in Brazil was presented. • Energy billed data and survey evidence were used to build a statistical stock model. • The Weibull distribution was the most adequate to represent the continuous variables. • kWh/student·year was more suitable than kWh/m2·year to model Brazilian school buildings stock. • Thermal performance is an issue to be greatly improved in schools in Brazil. Understanding the actual conditions in school buildings regarding energy usage and environmental satisfaction gives way to the development of consistent building stock modelling. In the Brazilian context, a deep-rooted inefficient structure of energy management implies a lack of mapping the actual conditions of the public schools. Therefore, this paper aimed to assess the actual energy performance of school buildings in Brazil. This paper innovates by integrating questionnaire responses and billed energy data to allow the construction of an evidence-based stock model through a statistical approach, which was never performed for the Brazilian building stock. Moreover, the association of energy use intensity (EUI) with the core aspects of building characteristics, occupancy, environmental satisfaction and necessary improvements mapped the relevance of such aspects in energy usage patterns. Additionally, a discussion of the EUI indicators was addressed. Results showed an unprecedented broad panorama of the school building stock in Brazil and its main features. In terms of statistical analysis, the Weibull distribution was appropriate for representing the continuous variables of the stock. The EUI as a function of the number of students was more suitable than the floor-plan area to achieve a reliable energy performance analysis. Several tests were performed to assess if the energy management, environmental satisfaction and maintenance practices impacted the EUI or not. Finally, the main conclusion was that the stock model obtained was simple and fit the energy management structure in the Brazilian scenario. [ABSTRACT FROM AUTHOR]

This paper presents the design of a Model Predictive Control (MPC) scheme to optimally manage the thermal and electrical subsystems of a small-size building (“smart house”), with the objective of minimizing the expense for buying energy from the grid, while keeping the room temperature within given time-varying bounds. The system, for which an experimental prototype has been built, includes PV panels, solar collectors, a battery pack, an electrical heater in a thermal storage tank, and two pumps on the solar collector and radiator hydraulic circuits. The presence of binary control inputs together with continuous ones naturally leads to using a hybrid dynamical model, and the MPC controller solves a mixed-integer linear program at each sampling instant, relying on weather forecast data for ambient temperature and solar irradiance. The procedure for controller design is reported with focus on the specific application, and the proposed method is successfully tested on the experimental site. [ABSTRACT FROM AUTHOR]

This paper proposes a novel two-stage Energy Management System (EMS) that is suitable for small-scale grid-connected electrical systems, such as smart homes and buildings, encompassing renewable generators and electrical storage. In such systems, forecast errors of renewable generation and energy demand profiles result in a significant uncertainty on the power exchanged between the end users and the utility grid. The proposed EMS reduces such demand uncertainty and the electricity bill for end users, at the same time. The main novelty of the proposed technique is that it does not require any change in pricing plans or user's habits, differently from classical Demand Side Management schemes. Moreover, thanks to the increased predictability of the exchanged power, utility providers are facilitated in managing the wholesale risk, for example by designing appropriate pricing schemes. The proposed EMS is based on an optimization algorithm. It starts from profiles of renewable generation and load demand, which are obtained by a forecasting method based on suitably chosen and trained Artificial Neural Networks. Furthermore, it has been designed to be suitable for an embedded implementation on low-performance processing platforms. The proposed EMS has been validated using datasets coming from monitoring campaigns. The considered case study is a smart home with an annual energy consumption of about 4500 kWh. It encompasses a grid-connected electrical distribution power plant with a 3 kW photovoltaic generator and a 4.6 kWh battery electrical storage system. The results obtained for a sample month demonstrate the effectiveness of the approach. As a matter of fact, the demand uncertainty is only 4.75% against a cumulative forecast error of 10.35% expressed as normalized root mean square error. At the same time, the end user's cash flow is 2.43% higher than the income obtained without an EMS. [ABSTRACT FROM AUTHOR]

Building energy simulation programs are powerful tools which are increasingly used at the design stage, for the assessment of compliance with energy efficiency regulations and for integration into energy management systems. A wider scope, a higher level of detail of building and thermal systems and the trend towards the use of dynamic simulations in real time are making these programs more computationally demanding. In this context, this paper presents a study of the computational time reduction in thermal simulations of buildings by applying equation tearing methods. These methods are employed to construct subsets of equations that can be independently solved with lower computational cost. The study was conducted at seven typical heating, ventilation, and air conditioning facilities. The computation time has been evaluated in five-minute intervals during a one-year period. The average saving ratio in processing time was 4.4; ratios ranged from 1.9 to 7.1. [ABSTRACT FROM AUTHOR]

Non-residential buildings like healthcare ones contribute to energy consumption and cause a negative environmental impact. This is mainly due to the age of the buildings, their poor level of energy efficiency and the implementation of only a basic maintenance plan. Owing to the very limited budget available for public administrators, Energy Performance Contracting (EPC), that entails the involvement of an Energy Service Company (ESCo), can provide the entire or part of the capital needed for investments aimed at progressively increasing energy efficiency over their service life. In this paper, three acute hospitals and two community clinics built in Italy are analysed, in order to assess the economic feasibility of several energy renovation actions that can be included within EPC contracts. To this purpose, the outcomes of energy audits carried out in 2014 about these buildings are reported, which involved analyses of consumption measured over the previous three years and assessment of use profiles for the development of models to break down the overall consumption and to estimate potential savings. Diverse improvement strategies were assessed, consisting in either isolated or combined renovation actions and tested on the five buildings, whose comparison was carried out in terms of energy saving, pollution reduction and economic feasibility. As a result, the best combined renovation actions per each hospital were recommended, that included – but were not limited to – the improvement of the control and regulation of existing sub-systems without replacement, partial replacement of other sub-systems and integration of renewables. Finally, each scenario was evaluated through the payback period and other economic performance indexes in order to assess the feasibility of the EPC framework when applied to this case study. [ABSTRACT FROM AUTHOR]

This paper presents a systematic, socio-technical and empirical evaluation of the actual energy and environmental performance of a sustainably-designed institutional building (Southeast England), intended to be a teaching tool and ‘living laboratory’ of sustainability. Despite the building being designed to high sustainability standards (Energy Performance Certificate rating of A, low reliance on fossil fuels, natural ventilation and rainwater harvesting) and also being under-utilised during the in-use stage (lower hours of occupation and number of occupants), its actual energy-related carbon dioxide-equivalent emissions are four times more than predicted. This is due to poor energy management of the building, underperformance of the biomass boiler and wasteful energy practices in terms of excessive winter overheating in the atrium, inappropriate lighting controls, and electrical equipment being left on standby. Due to lack of training and understanding of the energy manager, the building management system was not used adequately and issues with installation, commissioning and maintenance of the biomass boiler led to its disuse; however the photovoltaic system generated electricity as expected. Findings from the study show how a mixed-methods approach of building performance evaluation (BPE) should be embedded as part of the build process, to ensure that performance outcomes are met in reality. [ABSTRACT FROM AUTHOR]

A novel wallboard with double layers shape-stabilized phase change materials (SSPCMs) for year round energy management was proposed. Its energy saving potential in five typical climate regions of China was studied. One office room integrated with double layers SSPCMs wallboard was used for simulation platform. The other normal office room with the same envelope and just without double layers SSPCMs wallboard was used as reference room. Studies were conducted to investigate the energy saving potential of double layers SSPCMs wallboard in five cities representing five typical climate regions of China, including severe cold region, cold region, hot summer and cold winter region, hot summer and warm winter region, mild region. The optimal melting temperature and corresponding thickness of SSPCMs wallboard in five representative cities were given when the energy saving potential reached maximal value. It concluded that the optimal melting temperature of internal SSPCM layer was effected by indoor air temperature and the optimal melting temperature of external SSPCM layer was effected by outdoor air temperature. The characteristic of optimal values in the five representative cities were given for reference in this paper. [ABSTRACT FROM AUTHOR]

Energy Efficiency (EE) has become a common target for all buildings: the European Directive 2010/31 specifies that every new building must consume very little fossil energy (Nearly Zero Energy Building) by 2020; this deadline for public buildings is fixed by the end of 2018. However, modify the existing heritage is not easy. This is particularly true for historical public buildings belonging to the cultural heritage, because many energy solutions in the field of renovation are not compatible with historic constructions, which need to preserve authenticity and integrity. The paper discusses this critical issue by using a particular building in Italy: analysis, diagnosis and energy audits have been developed for the case study of the School of Engineering in Bologna, a representative building in the history of modern construction. Results of the microclimate monitoring campaign in different classrooms show how the lack of thermal control, together with poorly insulated envelopes’ components, determine high energy consumption. Selected modifications with a minimum impact have been considered for the energy retrofit. Results show a potential energy saving up to 32%, demonstrating how energy saving in historical buildings may be achieved by means of limited and non-invasive interventions on physical and material processes. [ABSTRACT FROM AUTHOR]

This paper investigates the Demand Side Management (DSM) in a commercial building microgrid with solar generation, stationary Battery Energy Management System (BESS) and gridable (V2G) Electric Vehicle (EV) integration. Taking into consideration of a comprehensive pricing model, we first formulate a deterministic DSM as a mixed integer linear programming problem, assuming perfect knowledge of the uncertainties in the system. A two-stage stochastic DSM is further developed that addresses the stochastic nature in solar generation, loads, EV availabilities and EV energy demands. The proposed DSMs are validated with real solar generation, loads, BESS and EV data using sample average approximation. Detailed case studies show that the stochastic DSM outperforms its deterministic counterpart for cost saving for a wide range of prices, though at the expense of higher computational time. Computational results also demonstrate that moderate number of EVs helps to cut down the overall operation cost, which sheds light on the benefit of future large scale EV integration to smart buildings. [ABSTRACT FROM AUTHOR]

Advances in sensor technologies and the proliferation of smart meters have resulted in an explosion of energy-related data sets. These Big Data have created opportunities for development of new energy services and a promise of better energy management and conservation. Sensor-based energy forecasting has been researched in the context of office buildings, schools, and residential buildings. This paper investigates sensor-based forecasting in the context of event-organizing venues, which present an especially difficult scenario due to large variations in consumption caused by the hosted events. Moreover, the significance of the data set size, specifically the impact of temporal granularity, on energy prediction accuracy is explored. Two machine-learning approaches, neural networks (NN) and support vector regression (SVR), were considered together with three data granularities: daily, hourly, and 15 minutes. The approach has been applied to a large entertainment venue located in Ontario, Canada. Daily data intervals resulted in higher consumption prediction accuracy than hourly or 15-min readings, which can be explained by the inability of the hourly and 15-min models to capture random variations. With daily data, the NN model achieved better accuracy than the SVR; however, with hourly and 15-min data, there was no definitive dominance of one approach over another. Accuracy of daily peak demand prediction was significantly higher than accuracy of consumption prediction. [ABSTRACT FROM AUTHOR]

Energy signatures for air conditioning systems can have characteristics which are not seen with heating systems. This paper explains and illustrates some of the characteristics that are specific to air conditioning systems and describes how energy signatures that take account of them can be applied to produce benchmarks, control charts and diagnostic information. It focusses on the use of energy signatures derived from measured daily system energy consumption. Daily energy signatures can generate more robust energy consumption benchmarks and provide additional insight into unusual energy demand patterns compared to monthly or weekly signatures, albeit requiring slightly more data. In particular, they distinguish between weekday and weekend consumptions. They can be used to generate benchmarks based on standardised annual consumption or standardised annual load factor. In addition they can be used to generate control charts to identify days of unusual consumption for individual systems. More sets of daily energy consumption data are needed to evaluate their diagnostic power. [ABSTRACT FROM AUTHOR]

With the advances of information technologies, today's building automation systems (BASs) are capable of managing building operational performance in an efficient and convenient way. Meanwhile, the amount of real-time monitoring and control data in BASs grows continually in the building lifecycle, which stimulates an intense demand for powerful big data analysis tools in BASs. Existing big data analytics adopted in the building automation industry focus on mining cross-sectional relationships, whereas the temporal relationships, i.e., the relationships over time, are usually overlooked. However, building operations are typically dynamic and BAS data are essentially multivariate time series data. This paper presents a time series data mining methodology for temporal knowledge discovery in big BAS data. A number of time series data mining techniques are explored and carefully assembled, including the Symbolic Aggregate approXimation (SAX), motif discovery, and temporal association rule mining. This study also develops two methods for the efficient post-processing of knowledge discovered. The methodology has been applied to analyze the BAS data retrieved from a real building. The temporal knowledge discovered is valuable to identify dynamics, patterns and anomalies in building operations, derive temporal association rules within and between subsystems, assess building system performance and spot opportunities in energy conservation. [ABSTRACT FROM AUTHOR]

The forecasting of building electricity demand is certain to play a vital role in the future power grid. Given the deployment of intermittent renewable energy sources and the ever increasing consumption of electricity, the generation of accurate building-level electricity demand forecasts will be valuable to both grid operators and building energy management systems. The literature is rich with forecasting models for individual buildings. However, an ongoing challenge is the development of a broadly applicable method for demand forecasting across geographic locations, seasons, and use-types. This paper addresses the need for a generalizable approach to electricity demand forecasting through the formulation of an ensemble learning method that performs model validation and selection in real time using a gating function. By learning from electricity demand data streams, the method requires little knowledge of energy end-use, making it well suited for real deployments. While the ensemble method is capable of incorporating complex forecasters, such as Artificial Neural Networks or Seasonal Autoregressive Integrated Moving Average models, this work will focus on employing simpler models, such as Ordinary Least Squares and k -Nearest Neighbors. By applying our method to 32 building electricity demand data sets (8 commercial and 24 residential), we generate electricity demand forecasts with a mean absolute percent error of 7.5% and 55.8% for commercial and residential buildings, respectively. [ABSTRACT FROM AUTHOR]

In this paper, a computational framework for occupancy detection and profiling based exclusively on depth data is presented. 3D depth sensors offer many advantages against traditional video cameras. Occupants’ privacy can be assured more effectively because depth information is unsuitable to reveal the person's identity. Notable low-level computer vision tasks can be simplified, thus lightening the computational load. The presented framework is suitable for wall-mounting setups as well as for ceiling-mounting setups, and scales well with the number of people. To take full advantage of depth data and to accommodate specificities of crowded environments, several improvements to the standard computer vision pipeline are suggested. Firstly, the running Gaussian average background model is adapted to work with depth distances in crowded scenes. Secondly, the classical complete linkage agglomerative clustering is boosted by adding edge-based constraints specifically designed for people segmentation in depth data. Thirdly, to reliable discriminate people, specific depth-based features are defined to be used with a Real AdaBoost classifier. The preliminary results achieved by using two different depth sensors and synthetic data are very promising, outperforming existing approaches. Relevant applications for building energy management, such as occupancy profiling and construction of trajectories and density maps, have been also demonstrated. [ABSTRACT FROM AUTHOR]

To foster the transition to more sustainable energy systems, policymakers have been approving measures to improve energy efficiency as well as promoting smart grids. In this setting, building managers are encouraged to adapt their energy operations to real-time market and weather conditions. Yet, most fail to do so as they rely on conventional building energy management systems (BEMS) that have static temperature set points for heating and cooling equipment. In this paper, we investigate how effective policy measures are at improving building-level energy efficiency compared to a smart BEMS with dynamic temperature set points. To this end, we present an integrated optimisation model mimicking the smart BEMS that combines decisions on heating and cooling systems operations with decisions on energy sourcing. Using data from an Austrian and a Spanish building, we find that the smart BEMS results in greater reduction in energy consumption than a conventional BEMS with policy measures. [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]

In this paper a generic model predictive control (MPC) algorithm for the management of stratified thermal storage tanks in buildings is proposed that can be used independently of the building's heat/cold generation, consumption and consumption control. The main components of the considered storage management are the short term load forecasting (STLF) of the heat/cold consumer(s) using weather forecast, the MPC algorithm using a bilinear dynamic model of the stratified storage and operating modes of the heat/cold generator(s), modeled as static operating points. The MPC algorithm chooses between these operating modes to satisfy the predicted cold demand with minimal costs. By considering the generator(s) in terms of operating modes the bilinearity in the storage model is resolved which leads to a hybrid MPC problem. For computational efficiency this problem is approximated by an iterative algorithm that converges to a close to optimal solution. Simulation results suggest that the approach is well suited for the use in buildings with a limited number of heat/cold generators. Additionally, the approach is promising for practical use because of its independence from the heat/cold consumer's control and because it requires limited information and instrumentation on the plant, i.e. low costs for control equipment. [ABSTRACT FROM AUTHOR]

Though many building energy benchmarking programs have been developed during the past decades, they hold certain limitations. The major concern is that they may cause misleading benchmarking due to not fully considering the impacts of the multiple features of buildings on energy performance. The existing methods classify buildings according to only one of many features of buildings—the use type, which may result in a comparison between two buildings that are tremendously different in other features and not properly comparable as a result. This paper aims to tackle this challenge by proposing a new methodology based on the clustering concept. The clustering concept, which reflects on machine learning algorithms, classifies buildings based on a multi-dimensional domain of building features, rather than the single dimension of use type. Buildings with the greatest similarity of features that influence energy performance are classified into the same cluster, and benchmarked according to the centroid reference of the cluster. The proposed methodology contains four steps: feature selection, clustering algorithm adaptation, results validation, and interpretation. The experimentation was carried out with a comparison between the proposed methodology and the Energy Star approach. It was shown that the proposed methodology could account for the total building energy performance and was able to provide a more comprehensive approach to benchmarking. In addition, the multi-dimensional clustering concept enables energy benchmarking among different types of buildings, and inspires a new perspective to investigate building performance typology. [ABSTRACT FROM AUTHOR]

Being increasingly insulated, new buildings are more and more sensitive to variations of solar and internal gains. Due to an important use of electrical heating systems, especially in housing, France is facing a growing problem of peak load on its electricity grid. Controlling the heating system often constitutes an efficient solution to shift heating loads while maintaining indoor comfort. The proposed energy management is a predictive set of optimal commands issued from a dynamic programming optimization knowing in advance the weather, occupancy and internal gains for the next 7 days. This method is tested on a low energy house situated in France with an annual heating demand of 14 kWh/m 2 . In this paper, load shifting according to utility rate incentives and carbon emissions are studied. The importance of building models as well as envelope insulation and thermal mass on energy management results is shown. [ABSTRACT FROM AUTHOR]

Data quality assurance of building energy consumption monitoring platform plays an important role in building energy consumption management. However, Data collected and transferred to the cloud platform is affected by many factors and shows some missing values and outliers in the time series. Combining with the correlation of smart meters on branches and regularity of building energy consumption characteristics, this paper proposes a synchronous prediction method for predicting building energy consumption in the secondary branch. In this model, synchronous data feature similarity (SDFS) model is used to find a similar energy consumption feature, Extreme gradient boosting (XGBoost) model is used to train and produce accurate prediction results which are compared with back propagation neural network (BPNN) and adaptive boosting (AdaBoost) and maximum distance outlier correction (MDOC) model can further correct the prediction results. Taking the daily energy consumption of the primary branch with a smart meter in the building energy consumption monitoring platform as the test object, the predition results of VRF energy consumption show that the MAE, MAPE, RMSE, and CVRMSE are 1.150, 0.142, 1.511, and 0.132 respectively, which is much lower than BPNN and AdaBoost. This study explores a novel feature mining method for historical data and an integrated model for outlier recognition and correction which significantly improves the accuracy of prediction results. Moreover, after correlation verification, the prediction model can be widely applied in building distribution system with sub-meter system which improves the data utilization rate of building energy management system. [ABSTRACT FROM AUTHOR]

An effective and accurate building energy consumption prediction model is an important means to effectively use building management systems and improve energy efficiency. To cope with the development and changes in digital data, data-driven models, especially deep learning models, have been applied for the prediction of energy consumption and have achieved good accuracy. However, as a deep learning model that can process high-dimensional data, the model often lacks interpretability, which limits the further application and promotion of the model. This paper proposes three interpretable encoder and decoder models based on long short-term memory (LSTM) and self-attention. Attention based on hidden layer states and feature-based attention improves the interpretability of the deep learning models. A case study of one office building is discussed to demonstrate the proposed method and models. Firstly, the addition in future real weather information yields only a 0.54% improvement in the MAPE. The visualization of the model attention weights improves the interpretability of the model at the hidden state level and feature level. For the hidden state of different time steps, the LSTM network will focus on the hidden state of the last time step because it contains more information. The Transformer model gives almost equal attention weight to each day in the coding sequence. For the interpretable results at the feature level, daily max temperature, mean temperature, min temperature, and dew point temperature are the four most important features. The four characteristics of pressure, wind speed-related features, and holidays have the lowest average weights. [ABSTRACT FROM AUTHOR]

The building sector consumes more than one third of the total final energy in the world. In order to tackle this issue, new UBEMs (Urban Building Energy Modeller) are developed to simulate, analyse or optimise the energy consumption at building and district scale. Such tools are useful for decision makers and urban planners, for the design, retrofit or energy management of districts and their energy systems. This paper describes DIMOSIM (District MOdeller and SIMulator), a modular UBEM to design and optimize district energy concepts. Based on an object structure, this tool creates a district from a GIS-file, completes missing data, simulates the buildings, systems and occupancy under a given weather to finally assess the simulation outputs. Thanks to the modular structure, it can be coupled to other tools to broaden the applications, as the simulation of microclimate or the use of model predictive controllers to optimize the control of districts. Several case studies are proposed, illustrating various applications of DIMOSIM. [ABSTRACT FROM AUTHOR]

Abstract: Building managers have specific duties and certain outputs that are required of them. Without the necessary data, information, tools, and time, they are unable to adequately meet their organisational goals. Scenario modelling enables explicit and unambiguous coupling of building functions with other pivotal aspects of building operation in a method that specifically considers the education and technical expertise of building managers. This new method captures, transforms, and communicates the complex interdependencies of environmental and energy management in buildings through an easily navigable, holistic, and reproducible checking mechanism that compares actual performance with predicted performance and completes the “plan-do-check-act” cycle for building managers. Most important, the structured nature of this method caters to the diverse profile of building managers, making it applicable for widespread deployment. This paper demonstrates the benefit of using the new method by examining its application to a performance analysis of two existing buildings. [Copyright &y& Elsevier]

Abstract: In an increasingly constrained context with severe requirements of quality, safety and environment respect, a building-integrated photovoltaic (BIPV) involves advanced local energy management rather than photovoltaic (PV) power permanent grid injection. This paper focus on building-integrated microgrid (BIMG) design and implementation applied to BIPV system with energy storage and smart grid communication. The goal is to propose power balancing strategy with smart grid interaction, aiming at reducing grid peak consumption, avoiding undesirable grid power injection, making full use of local PV production. The energy management is carried out considering grid time-of-use tariffs, grid access limits, storage capacity, load and PV power shedding if necessary. Based on experimental platform, the results show that the system maintains stability and confirm the relevance of the proposed local energy management giving perspectives on better integration of small PV plant in power grid. [Copyright &y& Elsevier]

Abstract: Model predictive control has become a widespread solution in many industrial applications and is gaining ground in the field of energy management and automation systems of buildings. A model with reasonable prediction properties is an ultimate condition for good performance of the predictive controller. This paper presents an approach in which a model of a building is selected by an iterative two stage procedure. In the first stage, a minimum set of disturbance inputs is formed so that the resulting model is the best with respect to a defined quality criterion; then the second stage comprises addition of the states to obtain the final minimum set of states maximizing the model quality. The procedure stops when it makes no sense to select more complex model as it brings no more quality improvements. Statistical tests such as the likelihood ratio test, the tests based on cumulative periodogram, the two-sample Kolmogorov–Smirnov test as well as others (fit factor and coefficient of determination) are used to evaluate the relationship between the addition of inputs/states and the model quality. Three identification approaches, namely model predictive control relevant identification, deterministic semi-physical and probabilistic semi-physical modeling are used for estimation of building parameters. Finally, a case study is provided where all the above mentioned approaches are investigated and tested. [Copyright &y& Elsevier]

Abstract: Buildings account for a relevant fraction of the energy consumed by a country, up to 20-40% of the yearly energy consumption. If only electricity is considered, the fraction is even bigger, reaching around 73% of the total electricity consumption, equally divided into residential and commercial dwellings. Building and Home Automation have a potential to profoundly impact current and future buildings’ energy efficiency by informing users about their current consumption patterns, by suggesting more efficient behaviors, and by pro-actively changing/modifying user actions for reducing the associated energy wastes. In this paper we investigate the capability of an automated home to automatically, and timely, inform users about energy consumption, by harvesting opinions of residential inhabitants on energy feedback interfaces. We report here the results of an on-line survey, involving nearly a thousand participants, about feedback mechanisms suggested by the research community, with the goal of understanding what feedback is felt by home inhabitants easier to understand, more likely to be used, and more effective in promoting behavior changes. Contextually, we also collect and distill users’ attitude towards in-home energy displays and their preferred locations, gathering useful insights on user-driven design of more effective in-home energy displays. [Copyright &y& Elsevier]

Organisations responsible for managing large stocks of residential social buildings spend significant resources on the energy usage for buildings and maintenance activities. The implementation of an energy management system compliant with ISO 50001 can yield more effective energy retrofit actions for buildings by improving the energy consumption and environmental performance of buildings through effective economic investment strategies. This paper describes an organisational methodology that complies with ISO 50001, applied for the first time to an Italian public body that manages a large building stock of social housing. In particular, this study examines the aspects concerning energy analysis of the stock and the definition of energy retrofit interventions towards heating load reduction based on consumption monitoring. The obtained results confirm the effectiveness of ISO 50001 as an organisational tool for energy management within a systematic approach. [ABSTRACT FROM AUTHOR]