Search

This paper addresses the problem of accurate and interpretable prediction of energy consumption in residential buildings. The solution that we propose in this work employs the knowledge discovery machine learning approach combining fuzzy systems with evolutionary optimization. The contribution of this work is twofold, including both methodology and experimental investigations. As far as methodological contribution is concerned, in this paper, we present an original designing procedure of fuzzy rule-based prediction systems (FRBPSs) for accurate and transparent energy consumption prediction in residential buildings. The proposed FRBPSs are characterized by a genetically optimized accuracy–interpretability trade-off. The trade-off optimization is carried out by means of multi-objective evolutionary optimization algorithms—in particular, by our generalization of the well-known strength Pareto evolutionary algorithm 2 (SPEA2). The proposed FRBPSs' designing procedure is our original extension and generalization (for regression problems operating on continuous outputs) of an approach to designing fuzzy rule-based classifiers (FRBCs) we developed earlier and published in 2020 in this journal. FRBCs operate on discrete outputs, i.e., class labels. The experimental contribution of this work includes designing the collection of FRBPSs for residential building energy consumption prediction using the data set published in 2024 and available from Kaggle Database Repository. Moreover, the comparison with 20 available alternative approaches is carried out, demonstrating that our approach significantly outperforms alternative methods in terms of interpretability and transparency of the energy consumption predictions made while remaining comparable or slightly superior in terms of the accuracy of those predictions. [ABSTRACT FROM AUTHOR]

Summary: Buildings around the world account for about one‐third of the energy consumption. Enough energy is required to maintain the comfort level for the occupants. Recently, the rise in global temperature resulting in climate change is associated with the comfort level for both outdoor and indoor of the buildings. Thus, providing acceptable comfort levels within buildings has become significant. The comfortable indoor environment of building requires energy for the operation of various appliances. A smart and energy efficient approach is the need of an hour to reduce energy consumption and attain comfortable indoor environment. The building energy and comfort management system (BECMS) model incorporating trade‐off between energy consumption and comfort has already been focused in previous studies. However, limited analyses have been observed in comparing the most efficient population‐based algorithm for BECMS. In this paper, a comparative study has been carried out using three different optimization techniques including multi‐objective genetic algorithm (MOGA), hybrid MOGA (HMOGA), and multi‐objective particle swarm optimization method (MOPSO) for optimal energy and comfort management in buildings. These optimization techniques have been widely employed to solve various optimization problems. The significant contribution of this article identifies the best suited algorithm in attaining best optimized solution of energy and comfort in a building. The comparative analysis of the three optimization techniques shows that MOPSO outperforms and attains maximum comfort level and higher energy savings. [ABSTRACT FROM AUTHOR]

Photovoltaic-thermal (PVT) technology refers to the integration of a photovoltaic (PV) and a conventional solar thermal collector, representing the deep exploitation and utilization of solar energy. In this paper, we evaluate the performance of a solar PVT cogeneration system based on specific building energy demand using theoretical modeling and experimental study. Through calculation and simulation, the dynamic heating load and electricity load is obtained as the basis of the system design. An analytical expression for the connection of PVT collector array is derived by using basic energy balance equations and thermal models. Based on analytical results, an optimized design method was carried out for the system. In addition, the fuzzy control method of frequency conversion circulating water pumps and pipeline switching by electromagnetic valves is introduced in this paper to maintain the system at an optimal working point. Meanwhile, an experimental setup is established, which includes 36 PVT collectors with every 6 PVT collectors connected in series. The thermal energy generation, thermal efficiency, power generation and photovoltaic efficiency have been given in this paper. The results demonstrate that the demonstration solar PVT cogeneration system can meet the building energy demand in the daytime in the heating season. [ABSTRACT FROM AUTHOR]

Works on the optimization of heating, ventilat ion and air-conditioning (HVAC) systems have been done extensively because of its high amount of building electrical energy usage. This paper provides a review on the optimizations works of HVAC systems based on three main approaches - HVAC operational parameters optimization, HVAC controller parameters optimization and building design parameters optimizat ion. For the system's operational parameters, the optimizat ion is based on the HVA C's conventional and predictive energy consumption models which is clear the predictive HVAC system models can get better response to reduce energy consumption compare to conventional energy consumption model. In most works, the thermal comfort model, either indicated by the indoor air quality (IAQ) or the predicted mean vote (PMV) was included. It is be noticed that between IAQ comfort index and PMV comfort index the PMV had a better response that can get 46% reduce the energy consumption. In addition, in the HVA C's controller optimization approach, its objective is to improve the output response of the HVAC system in order to avoid unnecessary energy usage by optimizing the controller parameters that employ controllers such as Fuzzy Logic, Neural Network and Proportional -Integral-Derivative (PID) controllers. It is clear that among the different controller optimizations mentioned above the fuzzy logic tuning optimization has a better response to reduction of energy consumption rather than other controller optimizat ion approach. Meanwhile, the optimizat ion of building design parameters approach is done before the construction of the buildings so as to reduce the energy consumption, where factors such as HVAC system type, construction material type and window dimensions are determined through the optimization process. This paper reviews the works based on the three approaches of HVAC system optimizat ions with the object ive of reducing energy usage without sacrificing the comfort of occupants inside the building that is recommended to us e predictive HVAC system approaches with fuzzy logic controller . Moreover, comparing different tools for building parameter and design optimization including SEDICAE, EXRETopt and EneryPlus, the EXRETopt by using PMV comfort index makes to 62% reduction of energy consumption. [ABSTRACT FROM AUTHOR]

Given the ability of a Building Information Model (BIM) to serve as a multi-disciplinary data repository, this paper seeks to explore and exploit the sustainability value of Building Information Modelling/models in delivering buildings that require less energy for their operation, emit less CO2?and at the same time provide a comfortable living environment for their occupants. This objective was achieved by a critical and extensive review of the literature covering: (1) building energy consumption, (2) building energy performance and analysis, and (3) building information modeling and energy assessment. The literature cited in this paper showed that linking an energy analysis tool with a BIM model helped project design teams to predict and create optimized energy consumption. To validate this finding, an in-depth analysis was carried out on a completed BIM integrated construction project using the Arboleda Project in the Dominican Republic. The findings showed that the BIM-based energy analysis helped the design team achieve the world's first 103% positive energy building. From the research findings, the paper concludes that linking an energy analysis tool with a BIM model helps to expedite the energy analysis process, provide more detailed and accurate results as well as deliver energy-efficient buildings. The study further recommends that the adoption of a level 2 BIM and the integration of BIM in energy optimization analyse should be made compulsory for all projects irrespective of the method of procurement (government-funded or otherwise) or its size. [ABSTRACT FROM AUTHOR]

The optimum sizing of photovoltaic technologies depends on certain variables such as the daily energy consumption of buildings and available solar potential of the location. The purpose of this paper is to define the optimum photovoltaic panel area with no battery system to supply the daytime electricity usage of a Vocational School in Sanlıurfa, Turkey. First, the maximum photovoltaic panel areas are found at the 100% self-consumption for the Mono-Si, Multi-Si, and CdTe photovoltaic technologies. Besides, for defining optimum installation powers, an economic analysis has been carried out. The seasonal performances of economical optimum capacities are investigated under the feed-in tariff scenario. At 100% self-consumption, the maximum photovoltaic panel areas are found 130 m², 160 m², and 170 m² for Mono-Si, Multi-Si and CdTe respectively. The results show that the installation of Mono-Si (115 m²), Multi-Si (150 m²), and CdTe (210 m²) photovoltaic systems at 1.65, 1.75, and 2.3 times as the daily peak electricity consumption, is the most optimal selection according to economic indicators. [ABSTRACT FROM AUTHOR]

Today's buildings are responsible for about 40% of total energy consumption and 30-40% of carbon emissions, which are key concerns for the sustainable development of any society. The excessive usage of grid energy raises sustainability issues in the face of global changes, such as climate change, population, economic growths, etc. Traditionally, the power systems that deliver this commodity are fuel operated and lead towards high carbon emissions and global warming. To overcome these issues, the recent concept of the nearly zero energy building (nZEB) has attracted numerous researchers and industry for the construction and management of the new generation buildings. In this regard, this paper proposes various demand side management (DSM) programs using the genetic algorithm (GA), teaching learning-based optimization (TLBO), the enhanced differential evolution (EDE) algorithm and the proposed enhanced differential teaching learning algorithm (EDTLA) to manage energy and comfort, while taking the human preferences into consideration. Power consumption patterns of shiftable home appliances are modified in response to the real-time price signal in order to get monetary benefits. To further improve the cost and user discomfort objectives along with reduced carbon emission, renewable energy sources (RESs) are also integrated into the microgrid (MG). The proposed model is implemented in a smart residential complex of multiple homes under a real-time pricing environment. We figure out two feasible regions: one for electricity cost and the other for user discomfort. The proposed model aims to deal with the stochastic nature of RESs while introducing the battery storage system (BSS). The main objectives of this paper include: (1) integration of RESs; (2) minimization of the electricity bill (cost) and discomfort; and (3) minimizing the peak to average ratio (PAR) and carbon emission. Additionally, we also analyze the tradeoff between two conflicting objectives, like electricity cost and user discomfort. Simulation results validate both the implemented and proposed techniques. [ABSTRACT FROM AUTHOR]

Energy efficiency is a critical element of building energy conservation. Energy Information Administration (EIA) and International Electrotechnical Commission (IEC) estimated that over 6% of electrical energy was lost during transmission and distribution. Sensing and tracking technologies, and data-mining offer new windows to better understanding these losses in real-time. Recent developments in energy optimization computational methods also allow engineers to better characterize energy consumption load profiles. The paper focuses on developing new and robust data-mining techniques to explore large and complex data generated by sensing and tracking technologies. These techniques would potentially offer new avenues to understand and prevent energy losses during transmission. The paper presents two new concepts: First, a set of clustering algorithms that model the supply-demand characterization of four different substations clusters, and second, a semi-supervised machine learning and clustering technique are developed to optimize the losses and automate the process of identifying loss factors contributing to the total loss. This three-step process uses real-time data from buildings and the substations that supply electricity to the buildings to develop the proposed technique. The preliminary findings of this paper help the utility service providers to understand the energy supply-demand requirements. [ABSTRACT FROM AUTHOR]

The building sector in the United States is responsible for 41% of energy consumption and 39% of carbon footprint while the majority of energy consumption and carbon footprint are caused by aging buildings which represent 70% of existing buildings in the United States. The energy consumption of aging buildings can be significantly reduced by identifying and implementing green building upgrade measures based on available budgets. Aging buildings are often in urgent need for upgrading to improve their operational, economic, and environmental performance. This paper presents the development of an optimization model that is capable of identifying the optimal selection of building upgrade measures to minimize energy consumption of aging buildings while complying with limited upgrade budgets and building operational performance. This optimization model is designed to estimate building energy consumption using energy simulation software packages such as eQuest and it is integrated with databases of building products. This optimization model performs analysis of replacing existing building fixtures and equipment during the optimization computations to identify the optimal replacement of building products that minimizes building energy consumption and carbon emissions. The model is designed to provide detailed results for building owners and operators, which include specifications for the recommended upgrade measures and their location in the building; upgrade cost; expected energy, operational, and life-cycle cost savings; and expected payback period. This paper illustrates the new and unique capabilities of the developed optimization model. [ABSTRACT FROM AUTHOR]

This paper presents the application of multi-objective genetic algorithms for holistic building design that considers multiple criteria; building energy use, capital cost, daylight distribution and thermal indoor environment. The optimization focus is related to building envelope parameters. To obtain relevant feedback from multi-objective optimizations in early design stages, evaluation speed is a key concern. The paper presents a fast evaluation method fit for the early design stages. It uses a combination of two different quasi-steady-state methods for energy and indoor environment evaluations, a Radiance implementation for daylight simulations and a scripted algorithm for capital cost evaluations. The application of the method is developed around an integrated dynamic model which allows visual design feedback from all evaluations to be an integrated part of the design tool experience. It is concluded, that quasi-steady-state methods implemented as part of integrated dynamic models are fast and flexible enough to support building energy-, indoor environment- and cost-optimization the early design stages. [ABSTRACT FROM AUTHOR]

Highlights: [•] The building integrated energy efficiency is proposed in the paper. [•] The counterbalance of load is proposed in the paper. [•] The building envelope exergy cost of unit construction area is set up. [•] The exergy of building envelope and cooling heating load is analyzed. [•] The scheme-selection optimization model is presented in the paper. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a method to determine the optimum capacity of distributed generation technologies for buildings. The Combined Heat and Power (CHP) unit has both absorption and electric chiller for cooling loads, and a supplementary boiler for heating loads. The demand is uncertain in the model. The underlying uncertainty is modeled using probabilistic theory. In this method, the expected value of the objective function is evaluated using numerical approaches. A simulation based optimization method is presented which can estimate the cost of system operation and find the appropriate design by means of stochastic programming. The effect of minimum economic turn-down of the facilities is also discussed in this paper. The optimum capacity of distributed generation technologies has a great effect on the cost of the building. Therefore, the proposed method is applied for a hospital in Iran as a case study. The results show that the optimum capacities of CHP and absorption chiller decreases and the capacities of the electric chiller and boiler rise by an increase in demand uncertainty. In addition, the appropriate minimum economic turn-down of facilities has a considerable effect on the overall system cost. [Copyright &y& Elsevier]

Abstract: This paper presents an optimization for hybrid PV/wind system based on loss of load probability (LLP) and system cost. This paper is divided into three main parts: optimization of PV array, wind turbine and battery, optimum PV array tilt angle and optimization of the inverter size. Firstly, the optimization performed is aimed to select the optimal capacities of the PV array and wind turbine that give minimum system cost. However, the optimization was done using a hybrid iterative/genetic algorithm. The iterative part of the proposed algorithm is applied to generate a set of possible configurations for the proposed system, while the genetic algorithm is applied to find the optimum configuration. Secondly, a model for optimizing the tilt angle of the PV array is presented based on the Liu and Jordan model. Finally, an iterative method for optimizing the inverter size in the proposed system is presented. The results showed that the optimum sizes of the PV array (C A ), wind turbine (C s ) and storage battery (C B ) are 1.1411, 0.7159, and 0.550, respectively, while the optimum inverter''s size (Rs) is 1.37. Lastly, by applying the calculated monthly optimum tilt angle, the collected yield of the PV array is increased by 7.96%. [Copyright &y& Elsevier]

The rural residences of Northwest China are characterized by a state of high energy consumption and low comfort due to the limited economic level and awareness of energy-saving compared with the urban residences. To remedy this, appropriate passive design strategies should be adopted first, in order to provide a design mode with low energy consumption and low cost for rural residences under the premise of thermal comfort. In this paper, taking Hanzhong region (Shaanxi Province, China) as an example, we establish a benchmark model based on a field survey and develop an optimization process by combining EnergyPlus simulation software, the MOBO optimization engine, and weighted sum method. The action mechanisms of passive design parameters, including the building orientation, length–width ratio, building envelope parameters, external shading system, and window–wall ratio, on heating, cooling, and total energy consumption are analyzed, and the quantitative relationships between single-parameter and energy consumption are established. Then, the mutually restricted indices of total energy consumption and initial investment cost are taken as optimization objectives, and 17 design parameters are selected as the optimization variables. The NSGA-II algorithm is adopted to conduct the multi-parameter, multi-objective optimization design for rural houses in Hanzhong area, through coupling of the EnergyPlus and MOBO software. In this way, Pareto solutions are obtained and the value distributions of the multi-objectives and design parameters are analyzed. Based on the actual requirements of decision-makers and using the weight method, three design schemes focusing on different performance tendencies are proposed. The results indicate that by using the proposed optimization process, the building energy consumption can be significantly reduced while taking initial investment costs into account, where the energy-saving rate is in the range of 31.9–61.5%. When the EC/IC ratio is 1:1, 2:1, and 1:2, the energy-saving rate can reach 51.5%, 57.8%, and 43.5%, respectively. It can provide a beneficial pattern for the energy-saving design and renovation of rural residences in Hanzhong area of China. [ABSTRACT FROM AUTHOR]

This paper presents a methodology, using Life Cycle Cost (LCC) optimization and building categorization, to achieve a systematic study of the cost-optimal energy efficiency potential (CEEP) for 920 listed buildings in the medieval district of Visby in Sweden. The aim is to study the CEEP and CO 2 emission reductions for this city that is included in the World Heritage List by UNESCO. The total CEEP is found to be 31% (20.6 GWh) resulting in a CO 2 reduction of 57% (33.3 kton). The categorization method showed that the buildings could be divided in four clusters depending on building material, geometry and layout. The LCC analysis revealed that the energy efficiency measure packages were cluster specific. It is shown that multi-story wood buildings (Cluster II) have lowest specific LCC, and would arguably be the starting point for a renovation process. Presently most of the studied buildings are connected to the district heating (DH). The results show that heat pump (HP) and wood boiler (WB) is cost-optimal heating system for multi-story stone and wood buildings, respectively. In order for the DH to compete with HP and WB, the DH price needs to be reduced by 23% and 16%. [ABSTRACT FROM AUTHOR]

More than 30% of the total primary energy in the world is consumed in buildings. It is crucial to reduce building energy consumption in order to preserve energy resources and mitigate global climate change. Building performance simulations have been widely used for the estimation and optimization of building performance, providing reference values for the assessment of building energy consumption and the effects of energy-saving technologies. Among the various factors influencing building energy consumption, occupant behavior has drawn increasing attention. Occupant behavior includes occupant presence, movement, and interaction with building energy devices and systems. However, there are gaps in occupant behavior modeling as different energy modelers have employed varied data and tools to simulate occupant behavior, therefore producing different and incomparable results. Aiming to address these gaps, the International Energy Agency (IEA) Energy in Buildings and Community (EBC) Programme Annex 66 has established a scientific methodological framework for occupant behavior research, including data collection, behavior model representation, modeling and evaluation approaches, and the integration of behavior modeling tools with building performance simulation programs. Annex 66 also includes case studies and application guidelines to assist in building design, operation, and policymaking, using interdisciplinary approaches to reduce energy use in buildings and improve occupant comfort and productivity. This paper highlights the key research issues, methods, and outcomes pertaining to Annex 66, and offers perspectives on future research needs to integrate occupant behavior with the building life cycle. [ABSTRACT FROM AUTHOR]

This paper evaluates the potential heating and cooling energy use and cost savings associated with improved controls of dynamic insulation materials (DIMs) when used in multilayered wall assemblies for commercial office buildings. Using a genetic algorithm (GA) based optimization technique, optimum R-value settings are identified for the DIMs throughout the year. These optimal R-value settings are selected to maximize energy cost savings when compared to traditional static insulated wall systems. It is found that optimal control settings for DIMs depend on several factors including building design and operation strategies as well as climatic conditions. Moreover, the optimal settings were found to depend on the orientation of exterior walls and well as the climatic conditions. Specifically, the analysis results indicate that north-facing walls are more active and frequently have to change their R-value in order to minimize the office building energy use and cost. When optimally controlled, DIMs could save up to 17% of annual heating and cooling energy costs for US office buildings. [ABSTRACT FROM AUTHOR]

In the design of low-energy buildings, mathematical optimisation has proven to be a powerful tool for minimising energy consumption. Simulation-based optimisation methods are widely employed due to the nonlinear thermal behaviour of buildings. However, finding high-quality solutions with reasonable computational cost remains a significant challenge in the building industry. In this paper, Ant Colony Optimisation for continuous domain (ACOR) is developed and applied to optimise a commercial building in Australia. The results for a typical commercial building showed that optimisation can achieve an additional energy savings of more than 11.4%, even after some common energy saving measures were implemented (e.g. double pane windows). The performance of ACOR was compared to three benchmark optimisation algorithms: Nelder-Mead (NM) algorithm, Particle Swarm Optimisation with Inertia Weight (PSOIW) and the hybrid Particle Swarm Optimisation and Hooke-Jeeves (PSO-HJ). This comparison showed that ACOR was able to consistently find better solutions in less time than the benchmark algorithms. The findings demonstrate that ACOR can further facilitate the design of low-energy buildings. [ABSTRACT FROM AUTHOR]

In this paper, a method for a multi-objective and simultaneous optimisation of building energy systems and retrofit is presented. Tailored to be suitable for the diverse range of existing buildings in terms of age, size, and use, it combines dynamic energy demand simulation to explore individual retrofit scenarios with an energy hub optimisation. Implemented as an epsilon-constrained mixed integer linear program (MILP), the optimisation matches envelope retrofit with renewable and high efficiency energy supply technologies such as biomass boilers, heat pumps, photovoltaic and solar thermal panels to minimise life cycle cost and greenhouse gas (GHG) emissions. Due to its multi-objective, integrated assessment of building transformation options and its ability to capture both individual building characteristics and trends within a neighbourhood, this method is aimed to provide developers, neighbourhood and town policy makers with the necessary information to make adequate decisions. Our method is deployed in a case study of typical residential buildings in the Swiss village of Zernez, simulating energy demands in EnergyPlus and solving the optimisation problem with CPLEX. Although common trade-offs in energy system and retrofit choice can be observed, optimisation results suggest that the diversity in building age and size leads to optimal strategies for retrofitting and building system solutions, which are specific to different categories. With this method, GHG emissions of the entire community can be reduced by up to 76% at a cost increase of 3% compared to the current emission levels, if an optimised solution is selected for each building category. [ABSTRACT FROM AUTHOR]

Lighting systems in buildings have substantial influence on both the occupants' productivity and energy efficiency. This paper addresses the problem of minimizing the energy necessary to provide predefined scheduled lighting levels upon a set of targets through an optimization technique. An experimental setup has been designed, built and used to gather data regarding the influences of five light sources upon three targets. The optimization problems corresponding to different lighting demands were solved with the recently introduced Competition Over Resources bio-inspired metaheuristic optimization algorithm. Modifications on this algorithm were proposed in order to enhance its performance in terms of convergence speed and computational effort. The modified algorithm was firstly tested with classical benchmark functions, and then with a real time lighting problem. Once algorithm performance improvements were verified, the energy consumption over a scheduled lighting demand was measured under different test conditions as to evaluate potential savings whilst minimizing the computational cost of running the algorithm. Using the proposed modifications, the best parameters for each lighting demand condition in the predefined schedule have been identified. By using these parameters along with a strategy of reusing the best solution while the lighting demand does not change, smaller population sizes in the algorithm could be used. Finally, the minimum algorithm population size at which energetic performance begins to be affected was identified. Promising results in terms of energy efficiency and reduced computational effort were achieved. [ABSTRACT FROM AUTHOR]

The increased use of renewable energy sources in buildings has resulted in the development of Zero Energy Buildings(ZEB). To assess the gaps for ZEB system integration and performance optimization,this paper summarizes a study undertaken to reveal potential challenges and opportunities for intelligent control system of ZEB, and comprehensively reviews current trends in building system performance optimization. The findings indicate a breakthrough of 3M (Micro-grid, Multi-agent, Multi-objective)system of ZEB for system integration and performance optimization and a concept of ZEB to Urban Heat Island(UHI) mitigation based on Smart Grid. [ABSTRACT FROM AUTHOR]

Real-time optimization (RTO) has been developed to improve the cost and/or energy efficiency of complex heating, ventilation and air-conditioning (HVAC) systems. In current literature, almost all of the developed real-time optimization methods belong to the type of time-driven optimization, in which the action of optimization is triggered by “time”. As optimization should be done when the system operating conditions experience a change that is large enough to cause current operational setting not optimal any more, optimization strategies should recognize ‘significant’ changes and perform optimization when necessary. Since the time-driven optimization is a periodic mechanism in nature while those ‘significant’ changes may not be periodic, the time-driven optimization cannot capture ‘significant’ changes and do the optimization promptly. Therefore, this paper proposes an event-driven optimization (EDO) for complex HVAC systems, the key idea of which is to use “event” rather than “time” to trigger the action of optimization. A systematic event-driven optimization method is illustrated, where its main tasks, including event definition and event identification, will be discussed. The proposed method will be compared with a conventional time-driven method using case studies, through which the main advantages of the proposed method will be identified. [ABSTRACT FROM AUTHOR]

This paper presents a model which simultaneously optimises the selection and operation of technologies for distributed energy systems in buildings. The Technology Selection and Operation (TSO) model enables a new approach for the optimal selection and operation of energy system technologies that encompasses whole life costing, carbon emissions as well as real-time energy prices and demands; thus, providing a more comprehensive result than current methods. Utilizing historic metered energy demands, projected energy prices and a portfolio of available technologies, the mathematical model simultaneously solves for an optimal technology selection and operational strategy for a determined building based on a preferred objective: minimizing cost and/or minimizing carbon emissions. The TSO is a comprehensive and novel techno-economic model, capable of providing decision makers an optimal selection from a portfolio of available energy technologies. The current portfolio of available technologies is composed of various combined heat and power (CHP) and organic Rankine cycle (ORC) units. The TSO model framework is data-driven and therefore presents a high level of flexibility with respect to time granularity, period of analysis and the technology portfolio. A case study depicts the capabilities of the model; optimisation results under different temporal arrangements and technology options are showcased. Overall, the TSO model provides meaningful insights that allow stakeholders to make technology investment decisions with greater assurance. [ABSTRACT FROM AUTHOR]

The primary objective of this paper is to demonstrate improved energy efficiency for domestic hot water (DHW) production in residential buildings. This is done by deriving data-driven optimal heating schedules (used interchangeably with policies) automatically. The optimization leverages actively learnt occupant behaviour and models for thermodynamics of the storage vessel to operate the heating mechanism – an air-source heat pump (ASHP) in this case – at the highest possible efficiency. The proposed algorithm, while tested on an ASHP, is essentially decoupled from the heating mechanism making it sufficiently robust to generalize to other types of heating mechanisms as well. Simulation results for this optimization based on data from 46 Net-Zero Energy Buildings (NZEB) in the Netherlands are presented. These show a reduction of energy consumption for DHW by 20% using a computationally inexpensive heuristic approach, and 27% when using a more intensive hybrid ant colony optimization based method. The energy savings are strongly dependent on occupant comfort level. This is demonstrated in real-world settings for a low-consumption house where active control was performed using heuristics for 3.5 months and resulted in energy savings of 27% (61 kW h). It is straightforward to extend the same models to perform automatic demand side management (ADSM) by treating the DHW vessel as a flexibility bearing device. [ABSTRACT FROM AUTHOR]

In this paper we consider the problem of optimizing the operation of a building heating system under the hypothesis that the building is included as an active consumer in a demand response program. Demand response requests to the building operational system come from an external market player or a grid operator. Requests assume the form of price–volume signals specifying a maximum volume of energy to be consumed during a given time slot and a monetary reward assigned to the participant in case it fulfills the conditions. A receding horizon control approach is adopted for the minimization of the energy bill, by exploiting a simplified model of the building. Since the resulting optimization problem is a mixed integer linear program which turns out to be manageable only for buildings with very few zones, a heuristics is devised to make the algorithm applicable to realistic size problems as well. The derived control law is tested on the realistic simulator EnergyPlus to evaluate pros and cons of the proposed algorithm. The performance of the suboptimal control law is evaluated on small- and large-scale test cases. [ABSTRACT FROM AUTHOR]

Most of the state-of-the-art building simulation programs implement models in imperative programming languages. This complicates modeling and excludes the use of certain efficient methods for simulation and optimization. In contrast, equation-based modeling languages declare relations among variables, thereby allowing the use of computer algebra to enable much simpler schematic modeling and to generate efficient code for simulation and optimization. We contrast the two approaches in this paper. We explain how such manipulations support new use cases. In the first of two examples, we couple models of the electrical grid, multiple buildings, HVAC systems and controllers to test a controller that adjusts building room temperatures and PV inverter reactive power to maintain power quality. In the second example, we contrast the computing time for solving an optimal control problem for a room-level model predictive controller with and without symbolic manipulations. Exploiting the equation-based language led to 2200 times faster solution. [ABSTRACT FROM AUTHOR]

Different control techniques together with intelligent building technology (Building Automation Systems) are used to improve energy efficiency of buildings. In almost all control projects, it is crucial to have building energy models with high computational efficiency in order to design and tune the controllers and simulate their performance. In this paper, a set of partial differential equations are formulated accounting for energy flow within the building space. These equations are then solved as conventional finite difference equations using Crank–Nicholson scheme. Such a model of a higher order is regarded as a benchmark model. An optimization algorithm has been developed, depicted through a flowchart, which minimizes the sum squared error between the step responses of the numerical and the optimal model. Optimal model of the construction element is nothing but a RC-network model with the values of Rs and Cs estimated using the non-linear time invariant constrained optimization routine. The model is validated with comparing the step responses with other two RC-network models whose parameter values are selected based on a certain criteria. Validations are showed for different types of building construction elements viz., low, medium and heavy thermal capacity elements. Simulation results show that the optimal model closely follow the step responses of the numerical model as compared to the responses of other two models. [ABSTRACT FROM AUTHOR]

Strategic decision making at the building level is gaining importance in the context of a more and more deregulated energy market. The increase of the set of available options regarding distributed and renewable energy technologies leads to a complex decision process. Importantly, such decision making process is affected by uncertainties and therefore stochastic models are needed. In this paper, a comprehensive deterministic strategic optimization model for energy systems planning at the building level is extended to a stochastic optimization framework, thereby allowing the decision maker to manage risks in addition to considering the variability of the uncertain parameters. A numerical example shows the importance of taking into account uncertainty and risk in this kind of problems. [ABSTRACT FROM AUTHOR]

The continuous increase in energy demand and the integration of a large number of renewable energy sources with intermittent production require an advanced energy management to guarantee an uninterrupted supply. Deployment of smart meters in wide areas of power networks and availability of affordable storage systems encourage development and implementation of local energy management systems. Local energy management is considered a feasible and inexpensive approach to reduce the impact of load variations and intermittent generation. Demand response and load shaping techniques provide customers with the facility to contribute to system balancing and improve power quality. This paper presents a building energy management which determines the optimal scheduling of the components of the local energy system. The developed two-stage optimization is based on a receding horizon strategy and minimizes the energy costs under consideration of the physical system constraints. The proposed building energy management has been implemented and tested with a medium-size hotel emulated in a microgrid testbed using power-hardware-in-the-loop techniques. The obtained results underline that energy management strategies can be validated under realistic conditions using flexible power scenarios. [ABSTRACT FROM AUTHOR]

Ground source heat pump systems have been proved to be one of the most efficient systems for heating and cooling in buildings. However, an optimal energy performance depends on a good control of the auxiliaries, which stand for an important part of the total energy consumption. The authors previously developed an experimental in situ optimization methodology for the water circulation pumps frequency of ground source heat pump systems when single stage and multi-stage ON/OFF regulation is employed. However, the user comfort was not completely met under extreme weather conditions during summer. This paper presents the upgrading of this energy optimization strategy combining circulation pumps frequency variation and building supply temperature compensation in order to ensure the user comfort while keeping high energy savings. Experimental results show that the user comfort is met by means of this new methodology and the energy savings (33%) are even higher than those obtained with the previous methodology. [ABSTRACT FROM AUTHOR]

Considerable research efforts in Building Optimization and Control (BOC) have been directed toward the development of “plug-and-play” BOC systems that can achieve energy efficiency without compromising thermal comfort and without the need of qualified personnel engaged in a tedious and time-consuming manual fine-tuning phase. In this paper, we report on how a recently introduced Parametrized Cognitive Adaptive Optimization – abbreviated as PCAO – can be used toward the design of both model-based and model-free “plug-and-play” BOC systems, with minimum human effort required to accomplish the design. In the model-based case, PCAO assesses the performance of its control strategy via a simulation model of the building dynamics; in the model-free case, PCAO optimizes its control strategy without relying on any model of the building dynamics. Extensive simulation and real-life experiments performed on a 10-office building demonstrate the effectiveness of the PCAO–BOC system in providing significant energy efficiency and improved thermal comfort. The mechanisms embedded within PCAO render it capable of automatically and quickly learning an efficient BOC strategy either in the presence of complex nonlinear simulation models of the building dynamics (model-based) or when no model for the building dynamics is available (model-free). Comparative studies with alternative state-of-the-art BOC systems show the effectiveness of the PCAO–BOC solution. [ABSTRACT FROM AUTHOR]

The butterfly optimization algorithm (BOA) is a novel meta-heuristic optimization algorithm, inspired by the intelligence foraging performance of butterflies. The aim of the current research is to minimize the energy consumption of an office building in Seattle using BOA. A heat transfer model of the building was modeled in EnergyPluse software and annual energy demand of the building was computed. A two-way coupling was established between EnergyPluse and BOA. The Energy-Pluse takes into account the non-linear interaction of design variables and computes the energy demand of the building. Then the computed amount of energy demand would be transferred to the BOA, where the optimization algorithm decides about changing the design variables. Then, a new set of design variables would be transferred to EnergyPluse for a new simulation. Through the dynamic interaction of BOA and EnergyPluse, a building with minimum energy demand was designed. The impact of the number of butterflies on the performance of the optimization algorithm was also investigated. It was found that using 50 butterflies would lead to the best optimization performance. A comparison between the present method and literature optimization methods was made, which showed that BOA with 15 butterflies or higher could adequately avoid local minimums and reach the best minimum with a reasonable computation effort. [ABSTRACT FROM AUTHOR]

Energy efficient operation of microgrids, a localized grouping of controllable loads with distributed energy resources like solar photovoltaic panels, requires the development of energy management systems (EMSs) with the capability of controlling the loads so as to optimize the aggregate performance of the microgrid. In microgrids comprising of buildings of different nature (residential, commercial, industrial, etc.), where the occupants exhibit heterogeneous occupancy schedules, the objective of an effective management strategy is to optimize the aggregate performance by intelligently exploiting the occupancy schedules and the intermittent production of solar energy. This paper presents a simulation-based optimization approach for the design of an EMS in grid-connected photovoltaic-equipped microgrids with heterogeneous occupancy schedule. The microgrid exchanges energy, buying or selling it, with the main grid and the EMS optimizes an aggregate multi-objective criterion that takes into account both the energy cost and the thermal comfort of the occupants of the microgrid. Simulative results obtained using a microgrid test case developed in EnergyPlus demonstrate the effectiveness of the proposed approach: the proposed EMS strategy is shown to take advantage of the occupancy information, intelligently and automatically changing the energy demand of each building according to the occupants’ behavior, and achieving relevant improvements with respect to alternative EMS strategies. [ABSTRACT FROM AUTHOR]

This paper discusses the maintenance plan optimization problem for the energy efficiency purpose in the building energy efficiency retrofitting context. A subproblem namely the Building Retrofitted Facilities Corrective Maintenance Planning (BRFCMP) problem is proposed, where the corrective maintenance for malfunctioning retrofitted items are involved. The aggregate performances of the homogeneous retrofitted item groups, instead of the individual item performances, are the main consideration of the optimization issue. An aggregate population level optimization model is proposed to address the BRFCMP problem. When further taking into account the uncertainties, the optimization problem is cast into an optimal control problem to reduce the consequent adverse impact, given the dynamic nature of the aggregate performances of the item groups during operation. Both the optimization and control system approaches are applied to solve the BRFCMP problem without or considering uncertainties. An actual building retrofitting project is used as the case study to investigate the important role of maintenance to the building energy efficiency. The effectiveness of the proposed approaches is verified by simulation results. [ABSTRACT FROM AUTHOR]

Low energy buildings and zero energy buildings have attracted increasing attention in both academic and professional fields. The performances of these buildings are largely affected by the design of the renewable energy systems. This paper presents a comparison study on two design optimization methods for renewable energy systems in these buildings, including a single objective optimization using Genetic Algorithm and a multi-objectives optimization using Non-dominated Sorting Genetic Algorithm (NSGA-II). Building energy system models and renewable energy system models are developed and adopted, allowing the consideration of the interaction between building energy systems and renewable energy systems in optimization. Two case studies are conducted to evaluate the capability and effectiveness of proposed optimization methods, based on the Hong Kong Zero Carbon Building. The performances of the buildings with the renewable energy systems optimized by both methods are much better than that of the benchmark building in most scenarios. The single objective optimization can provide the “best” solution directly for a given objective while the multi-objective optimization provides rich information for designers to make better compromised decisions. [ABSTRACT FROM AUTHOR]

The recast version of the Energy Performance of Buildings Directive (2010/31/EU) proposes a comparative methodology aimed at defining the energy performance of buildings “with a view to achieving cost-optimal levels”. Really, how can the cost-optimal technologies be detected? Moreover, how can the most proper packages of energy efficiency measures be chosen in order to obtain the cost-optimality? This paper would provide answers to these questions, by proposing a new methodology for the evaluation of the cost-optimality, by means of the multi-objective optimization of energy performance of buildings and indoor thermal comfort. The optimization procedure is developed by means of the coupling between MatLab and EnergyPlus, by implementing a genetic algorithm, and it allows the evaluation of profitable and feasible packages of energy efficiency measures applied to buildings. Then, following the adoption of these packages, the global cost over the lifecycle of the building is calculated, in order to identify the cost-optimal solution. After the presentation of the methodology, the developed method is proposed for the optimization of the energy retrofit of an existing building. Furthermore, the method can be applied also for new architectures, by considering reference buildings. [ABSTRACT FROM AUTHOR]

Evolutionary genetic optimization algorithms (GA) have been used for thermal building optimization in the past. However, the results of these algorithms can differ significantly from each other because of random search and it is not guaranteed that the optimal solution is close to the global optimum. Furthermore, the use of these algorithms for non-expert users is limited. In this study, a hybrid single objective building optimization algorithm is introduced, which combines an evolutionary genetic algorithm with a modified simulated annealing algorithm. The goal of this paper is (1) to illustrate that the GA does not always provide solutions close to the global optimum and (2) to provide a building optimization method, which provides a higher reliability than what the GA alone can provide by using a relatively short computation time. Results illustrate that the hybrid GA coupled with the modified SA provides solutions close to the global optimum in all of the test runs in this study. The proposed algorithm therefore provides more reliable results than the GA without the addition of the modified SA. [ABSTRACT FROM AUTHOR]

For military and disaster relief housing, rapidly deployable shelters must be lightweight, be packaged in a small volume for transportability, and be erected without heavy lifting equipment. A critical design criterion is also energy efficiency in heating and cooling. To meet these priorities, the research team has utilized origami as inspiration for a thermally insulated rigid wall deployable shelter that can be erected manually through counterweighting. To enhance energy efficiency, improvements in the shape of a structure (i.e., member lengths and angles) at a design stage can lead to savings throughout its lifecycle. This is magnified in the context of mass-production of deployable shelters, where any improvements are multiplied. Structural efficiency is also critical to achieve lightweight design. This paper presents a multi-objective shape optimization methodology which balances the priorities of structural performance (i.e., minimum deflections) and energy efficiency (i.e., minimum thermal energy load). This is demonstrated for the case study of a deployable shelter. Design variables include geometric parameters. Constraints relate to the package size and capability of interfacing with existing technologies. Structural analysis is performed using a parametric finite element program. Thermal energy is calculated using EnergyPlus. An optimized solution is found and compared against existing military solutions. [ABSTRACT FROM AUTHOR]

In this paper the novel approach of a fuzzy model-based multivariable predictive functional control (FMBMPC) of a heating ventilating and air conditioning (HVAC) system is presented, which is implemented on a real-world test plant. The control law is derived in the state-space domain and is given in an analytical form without an optimization algorithm. The basic principles of the predictive control were extended in a fuzzy multivariable manner and the suggested tuning rules for the proposed control algorithm were depicted, which normally gives satisfactory results. The proposed approach introduces a compact and relatively simple design in the case of higher-order and nonminimal phase plants, but it is limited to open-loop stable plants. For the comparison a classical optimal proportional–integral (PI) controller was also designed and applied. The results show that the FMBMPC approach performs better due to the HVACs’ nonlinear dynamics. In case of interactions influence rejection by the HVAC system, the FMBMPC algorithm outperforms the classical PI approach. The results also show that the proposed approach exhibits better reference-model tracking across a wider operating range. The energy consumption comparison shows that the FMBMPC approach is also more energy-efficient. A shortened literature review of applications of energy-efficient and MPC control for HVAC systems is also presented. FMBMPC control is interesting in the case of batch reactors, furnaces, pressure vessels, HVAC systems and any processes that have strong nonlinear dynamics, multivariable natures and long transport delays. [ABSTRACT FROM AUTHOR]

The use of software tools to aid building design, or to show compliance, is now commonplace. This has motivated investigations into the potential of optimisation algorithms, used in such software, to automatically optimise designs, or to generate a variety of near-optimal designs. Optimisation always requires the evaluation of a large number of possibilities, before a final selection is made. Normally when using a building simulator to assess the quality of designs, all possible solutions in the early stages of optimisation (when there is a high volume of choices) are evaluated using the same tool, so that the computational time for the assessment of each of the possibilities is the same as the time required for the final, refined choice of solutions. This paper suggests using a method of evaluation which changes as the algorithm evolves: whereas accuracy is initially compromised to improve the speed of the algorithm, the process is subsequently altered to produce a more accurate, evaluation process. This is a case of dynamic optimisation that requires an algorithm able to cope with changes in the objective landscape. A self-adaptive evolutionary strategy has been chosen, for its ability to “learn” about changes, and the influence of the different decision variables in the objective function as they arise. The results show that this method can reach the same optimal design, with substantially lower computational time than the optimisation methods found in the literature. [ABSTRACT FROM AUTHOR]

Retrofitting of existing buildings offers significant opportunities for improving occupants’ comfort and well-being, reducing global energy consumption and greenhouse gas emissions. This is being considered as one of the main approaches to achieve sustainability in the built environment at relatively low cost and high uptake rates. Although a wide range of retrofit technologies is readily available, methods to identify the most suitable set of retrofit actions for particular projects are still a major technical and methodological challenge. This paper presents a multi-objective optimization model using genetic algorithm (GA) and artificial neural network (ANN) to quantitatively assess technology choices in a building retrofit project. This model combines the rapidity of evaluation of ANNs with the optimization power of GAs. A school building is used as a case study to demonstrate the practicability of the proposed approach and highlight potential problems that may arise. The study starts with the individual optimization of objective functions focusing on building's characteristics and performance: energy consumption, retrofit cost, and thermal discomfort hours. Then a multi-objective optimization model is developed to study the interaction between these conflicting objectives and assess their trade-offs. [ABSTRACT FROM AUTHOR]

Abstract: Buildings are one of the leading energy consumers in Europe. To improve energy efficiency in the built environment means to optimise the operation of building systems. Acoustics potentially provide an important information source to support the assessment of the operation of buildings. Recently, different approaches to the application of acoustic sensing technology have been studied. This paper presents an overview of three on-going projects which look at how sounds can be utilized to reduce energy consumption in buildings. In the “Sounds for Energy Control of Buildings” (S4ECoB) project the primary goal is the establishment of more energy-efficient buildings through the optimization of existing BMS. The “Experimenting Acoustics in Real environment using Innovative Test-beds” (EAR-IT) project aims to develop acoustic mass flows maps which provide awareness of the flows of masses. The “Energy efficient & Cost competitive retrofitting solutions for Shopping buildings” (EcoShopping) project purpose is to increase the energy efficiency of a building and to optimize the investments necessary in building retrofitting. Diverse methodologies and technologies (wired, wireless and mobile) are tested in realistic conditions provided by each demonstrator. [Copyright &y& Elsevier]

Abstract: Energy efficiency optimization ICT (Information and Communication Technology) solutions are currently being developed for energy saving in buildings and, to some ex-tent, also for the manufacturing domain. This paper describes an approach and ICT tool developed for manufacturing process energy efficiency optimization, in particular focused on the heat treatment process of steel casting parts. Traditionally this manufacturing process is designed based on experts experience selecting a predefined temperature-time curve provided customer specifications for the resulting steel parts. However this curve can actually be optimised in terms of energy consumption while keeping required mechanical properties. This improved design is what the tool here described provides, using knowledge based approach for process design and multivariate optimisation and simulation techniques for process optimisation. [Copyright &y& Elsevier]

The building enclosure plays a relevant role in the management of the energy flows in buildings and in the exploitation of solar energy at a building scale. An optimized configuration of the façade can contribute to reduce the total energy demand of the building. Traditionally, the search for the optimal façade configuration is obtained by analyzing the heating demand and/or the cooling demand only, while the implication of the façade configuration on artificial lighting energy demand is often not addressed. A comprehensive approach (i.e. including heating, cooling and artificial lighting energy demand) is instead necessary to reduce the total energy need of the building and the optimization of the façade configuration becomes no longer straightforward, because non-linear relationships are often disclosed. The paper presents a methodology and the results of the search for the optimal transparent percentage in a façade module for low energy office buildings. The investigation is carried out in a temperate oceanic climate, on the four main orientations, on three versions of the office building and with different HVAC system’s efficiency. The results show that, regardless of the orientations and of the façade area of the building, the optimal configuration is achieved when the transparent percentage is between 35% and 45% of the total façade module area. The highest difference between the optimal configuration and the worst one occurs in the north-exposed façade, while the south-exposed façade is the one that shows the smallest difference between the optimal and the worst configuration. [ABSTRACT FROM AUTHOR]

Thermal comfort has become a topic issue in building performance assessment as well as energy efficiency. Three methods are mainly recognized for its assessment. Two of them based on standardized methodologies, face the problem by considering the indoor environment in steady-state conditions (PMV and PPD) and users as active subjects whose thermal perception is influenced by outdoor climatic conditions (adaptive approach). The latter method is the starting point to investigate thermal comfort from an overall perspective by considering endogenous variables besides the traditional physical and environmental ones. Following this perspective, the paper describes the results of an in-field investigation of thermal conditions through the use of nearable and wearable solutions, parametric models and machine learning techniques. The aim of the research is the exploration of the reliability of IoT-based solutions combined with advanced algorithms, in order to create a replicable framework for the assessment and improvement of user thermal satisfaction. For this purpose, an experimental test in real offices was carried out involving eight workers. Parametric models are applied for the assessment of thermal comfort; IoT solutions are used to monitor the environmental variables and the users’ parameters; the machine learning CART method allows to predict the users’ profile and the thermal comfort perception respect to the indoor environment. [ABSTRACT FROM AUTHOR]