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New smart building technologies that offer continuous dynamic optimization of Heating, Ventilation, and Air Conditioning (HVAC) control hold promise to advance building operations for efficiency and grid response. These technologies use data from the control system to determine the analytically optimal setpoints, and then write back the optimal setpoints into the control system to minimize system energy consumption or costs. There are limited studies documenting field validations of these technologies. This paper presents the results from a long-term field evaluation of a model-predictive HVAC optimization system that installed in four commercial buildings. Energy savings analysis was conducted based on pre/post submetered energy use. Across the cohort of evaluation sites, HVAC savings following the implementation of the optimization system were mixed, ranging from 0–9%. Analysis of site operational data showed that occupant comfort was neither positively nor negatively impacted. Key technology adoption considerations and recommendations are summarized in the paper. The technology performs best when HVAC systems are in good working condition, and can be exercised to achieve the full range of its optimized setpoints–however it may not provide extensive additional savings over cases where best practice sequences of operation and reset strategies are already comprehensively implemented. [ABSTRACT FROM AUTHOR]

The implications of optimised structural designs in the life cycle carbon performance of buildings have been systematically overlooked in previous studies. The paper addresses this common limitation offering an integrated sustainable structural analysis at building level. To achieve this, a BIM-embedded approach was established utilising embodied carbon metrics and results from heuristic structural optimisation. A real building scenario was used to test the proposed approach in the context of multi-storey reinforced concrete (RC) buildings. Results show how the building life cycle performance is affected by the use of structurally optimised designs. The structural floors in particular, not only cover the largest proportion of the embodied carbon in the structure but they are also responsible for a large proportion of the carbon emissions of the tested building elements. The results obtained in this paper justified the need for more comprehensive efforts in the design optimisation of RC floors. Overall, it is suggested that the unclear interpretation of the optimisation outputs would result in the selection of structural designs that could compromise the buildings carbon performance. [ABSTRACT FROM AUTHOR]

The objective of this paper is to optimize the passive design of newly-built residential buildings in hot summer and cold winter region of China for improving indoor thermal comfort while reducing building energy demand. In this respect, this paper investigates the performance of a representative apartment building in the city of Shanghai and evaluates the optimum solutions by using a developed optimization approach, which includes three major steps of 1) setting the model for multi-objective optimization, 2) sensitivity analysis for reducing the dimension of input variables, and 3) multi-objective optimization by using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) coupled with the Artificial Neural Network (ANN), among which a novel indicator for evaluating the annual indoor thermal comfort of residential buildings of Shanghai named Comfort Time Ratio (CTR) is defined based on the modification of Szokolay’s theory in terms of bioclimatic analysis, and the impacts of passive design variables on the indoor thermal comfort and building energy demand in terms of different directions are comprehensively investigated. Results of the multi-objective optimization indicate that the residential buildings of Shanghai have a great potential in comfort-improvement and energy-saving. A series of novel optimal passive design tactics for residential buildings in Shanghai are derived accordingly which could be easily understood and conveniently carried out by the architects in practice. [ABSTRACT FROM AUTHOR]

• A real-time operational optimization for district cooling system operations. • Established a holistic optimization comprising of every component of the system. • Implemented the proposed optimization in a live district cooling plant in Singapore. • Achieved 10% efficiency improvement and 2.9 million kWh of electric energy saving. • A complete holistic district cooling system optimization at least in South East Asia region. This paper is proposing a real-time optimization system for the district cooling systems which comprises of three different modules being the cooling demand prediction and guidance, chiller system optimization, and cooling tower system optimization modules. First and foremost, the cooling demand for a district cooling system (DCS) is predicted by engaging the artificial Neural Network technique. The Mixed Integer Linear Programming technique then does its job to help in defining the optimal chiller dispatch schedule and allocation of cooling loads among chillers in the district cooling plant. Afterwards, the previously predicted demand is introduced into the chiller system and cooling tower system optimization modules to define an optimal operation in real-time. The regression technique and historical operation data are employed to capture the characteristics and operating performance of each component present in the DCS plant. An objective function, i.e., minimization of energy consumption, is established for the carrying out of the optimization by applying the Sequential Quadratic Programming. The proposed optimization is then deployed to a commercial DCS plant in Singapore. Subsequently, two years' worth of operating data are collected and compared. Three important contributions are presented. The first contribution is that the proposed intelligent operation strategy offers approximately 10% energy efficiency improvement. The second contribution is creating detailed mathematical models and defining the important interrelations of different DCS components. The last contribution is a holistic DCS optimization coupling with demand prediction. The latter two contributions allow to avoid suboptimal solution and grasp an optimal operation from different performance combination of DCS components. A practical implementation in a commercial DCS plant is done and offers 2.4 million kWh of annual electric energy saving. [ABSTRACT FROM AUTHOR]

The energy efficiency in the building sector, a widely accepted point inside world energy sustainability, can be pursued in a variety of complementary ways, being energy consumption reduction one of them. Specifically, the residential buildings are responsible for more than 40% of the total energy consumption of the European Union. Therefore, this residential sector is an important target for optimizing the use of energy. This paper examines the energy saving measures for multi-family residential building thermal systems in the city of Granada. The state and usage patterns of such systems in a middle-size city are surveyed and analysed in order to propose sounded saving measures. The proposed measures were quantified according to two parameters: energy saved and CO 2 emissions reduction. This quantification enables the identification of best measures either cost or energy effective, concluding that the biggest benefits are obtained adopting solar/biomass energy, while other zero-cost measures like the information campaign have more impact than, for instance, boiler replacement. Finally, it is concluded that an extension of mandatory installation of solar systems to existing buildings should be considered. [ABSTRACT FROM AUTHOR]

In this paper we propose a novel and easily implementable model-based air balancing method for a ventilation system. The proposed method provides a well-defined form of balancing for a ventilation system, which can be effectively solved. The method follows three procedures: (i) mathematical modeling for duct system which is based on steady-state pressure balance; (ii) model parameters identification by supervised machine learning; (iii) determination of damper positions based on the model. The performance of this method is validated through testing in a duct testbed with five terminals. The results show that the final flow rates in all terminals match with the desired values with no more than 6.8% relative error. [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]

This paper addresses the issue of an intermittent primary energy source in several developing countries. A PV-battery backup system is modeled and assessed as a replacement of the utility energy during daily power outage periods in a residential house. This paper focuses on the sizing of the PV system. The main objective is the minimization of the overall cost of the system over a 20 year period. One prime innovation in this study is that this system operates in conjunction with the grid, allowing it to charge the battery bank along with the PV panels whenever needed. Therefore, the optimization procedure takes into account more severe conditions compared to a classic standalone PV-battery system. The optimization is conducted based on real weather data, realistic residential load profiles and power outage schedules. Technical constraints are integrated in the algorithm. Two robust optimization techniques are used for comparison and validation purposes: the Genetic Algorithm and the Particle Swarm Optimization. In addition, two different situations are considered: harsh and moderate conditions. The simulation results validate the feasibility of this installation, and prove that such systems are affordable and well suited for the studied case. [ABSTRACT FROM AUTHOR]

The energy consumption in a multi-chiller system is very significant and its consumption will be increased by inappropriate operating of the chillers. Accordingly, the economic dispatch in multi-chiller systems becomes an important issue for saving energy. Such issue is known as optimal chiller loading, which is solved by applying different optimization methods. This study employs exchange market algorithm to obtain optimal set points of the economic dispatch problem on chiller plants, which is inspired by trading the shares on a stock market. Exchange market algorithm is proposed for solving continuous non-linear optimization problems and it is comprised of two searching and two absorbing operators. The decision variable of the optimal chiller loading is partial load ratio of each chiller and the objective function of the optimal chiller loading problem is minimizing the power consumption while satisfying the cooling demand. This paper adopts three case studies to evaluate the performance of exchange market algorithm and compares it with other optimization methods applied on the optimal chiller loading problem. Results show that exchange market algorithm is an efficient method for solving optimal chiller loading problem in chiller plants, and is adaptable for usage in air-conditioning systems. In addition, the comparison results show that the exchange market algorithm provides better solutions than previous optimization methods applied on the optimal chiller loading method in terms of convergence speed and total consumption of electrical energy. [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]

With the increased complexity of air conditioning systems, the computational load and complexity of real-time optimization appear to be challenging for applications. To overcome the challenge, multiplexed optimization has been proposed in which multiple set-points are sequentially optimized and updated one by one. In such a way, it can achieve equivalently-good energy performance but with exponentially reduced computational load if compared with those traditional methods that optimize all set-points simultaneously. In fact, the time instant for each set-point optimization and the set-point optimization sequence have impacts on the overall performance of the multiplexed optimization. Considering such impacts, this paper proposes a new multiplexed optimization method for enhanced performance. In the proposed method, optimization is activated as the system energy efficiency is low. The set-point optimization sequence is selected according to their impacts on the system energy performance. The one with the most energy saving potential will be firstly optimized. The case study results demonstrate the improved performance of the proposed method in terms of energy, computation load and system stability when compared with the conventional multiplexed optimization. The proposed method can be applied in complex air conditioning systems for energy saving, computation load reduction and system stability improvement. [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 the last years, multi-objective optimization techniques became into one of the main challenges of the building energy efficiency area. The objective of this paper is to develop and validate a computational code for multi-objective building performance optimization by linking an evolutionary algorithm and a building simulation software in a powerful cluster. A sophisticated version of the multi-objective Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was implemented in Python code to determine the optimal building design, which allows working with categorical and discrete variables, and the objectives were evaluated using the building energy simulation software EnergyPlus. NSGA-II was implemented to run in a high-performance cluster for the parallel computing of the fitness of each population (set of possible designs). In this work, the strengths of the proposed method were demonstrated by its application to the optimal design of a typical single-family house, located in the Argentine Littoral region. This house has some rooms conditioned only by natural ventilation, and other rooms with natural ventilation supplemented by mechanical air-conditioning (hybrid ventilation). The most influential design variables like roof types, external and internal wall types, solar orientation, solar absorptance, size, type, and windows shading of this house among others were studied in two complex cases of 10 8 and 10 16 possibilities to obtain the best trade-off (Pareto front) between heating and cooling performance. Finally, a decision-making method was applied to select one configuration of the Pareto front. Optimal simulation results for the study cases indicated that is possible to improve up to 95% the thermal comfort in naturally ventilated rooms and up to 82% energy performance in air-conditioned rooms of the building with respect to the original configuration by using a design that takes simultaneous advantage of passive strategies like thermal inertia and natural ventilation. The methodology was proved to give a robust and powerful tool to design efficient dwellings reducing the optimization time from almost 12 days to 4.4 h. [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: Despite the large economic and ecologic costs, museum storage spaces are often equipped with extensive air conditioning, to provide the desired stable interior climate. The new “passive conditioning” paradigm aims at resolving these costs: a high-hygrothermal-inertia building with a high-hygrothermal-resistance envelope is to satisfactorily stabilise that interior climate, with no need for mechanical air conditioning hence. This paper first studies the reliability of full passive conditioning for museum storage spaces. It is shown to be an illusion, since it usually results in excessive interior humidities. Auxiliary dehumidification is thus required to provide good conservation conditions, and it is secondly investigated how the dehumidification load can be diminished. The study reveals the crucial impact of air tightness, while thermal insulation only has a minor influence. To further reduce the economic and ecologic cost of conditioning, the paper finally assesses “concentrated dehumidification”: dehumidification during a part of the day only, while leaving the humidity free-running during the rest of the day. It is established that the hygric inertia of the interior air, building walls and stored objects keeps the fluctuations of the interior relative humidities to a minimum when considering adequately air tight museum storage spaces. [Copyright &y& Elsevier]

A basic but crucial step to increase efficiency and save energy in residential settings is to have an accurate view of energy consumption. To monitor residential energy consumption cost-effectively, i.e., without relying on per-device monitoring equipment, non-intrusive load monitoring (NILM) provides an elegant solution. The aim of NILM is to disaggregate the total power consumption (as measured, e.g., by smart meters at the grid connection point of the household) into individual devices’ power consumption, using machine learning techniques. An essential building block of NILM is event detection: detecting when appliances are switched on or off. Current state-of-the-art methods face two open issues. First, they are typically not robust to differences in base load power consumption and secondly, they require extensive parameter optimization. In this paper, both problems are addressed. First two novel and robust algorithms are proposed: a modified version of the chi-squared goodness-of-fit ( χ 2 GOF) test and an event detection method based on cepstrum smoothing. Then, a workflow using surrogate-based optimization (SBO) to efficiently tune these methods is introduced. Benchmarking on the BLUED dataset shows that both suggested algorithms outperform the standard χ 2 GOF test for traces with a higher base load and that they can be optimized efficiently using SBO. [ABSTRACT FROM AUTHOR]

Home energy management system (HEMS) is essential for residential electricity consumers to participate actively in demand response (DR) programs. Dynamic pricing schemes are not sufficiently effective for end-users without utilizing a HEMS for consumption management. In this paper, an intelligent HEMS algorithm is proposed to schedule the consumption of controllable appliances in a smart household. Electric vehicle (EV) and electric water heater (EWH) are incorporated in the HEMS. They are controllable appliances with storage capability. EVs are flexible energy-intensive loads, which can provide advantages of a dispatchable source. It is expected that the penetration of EVs will grow considerably in future. This algorithm is designed for a smart household with a rooftop photovoltaic (PV) system integrated with an energy storage system (ESS). Simulation results are presented under different pricing and DR programs to demonstrate the application of the HEMS and to verify its’ effectiveness. Case studies are conducted using real measurements. They consider the household load, the rooftop PV generation forecast and the built-in parameters of controllable appliances as inputs. The results exhibit that the daily household energy cost reduces 29.5%–31.5% by using the proposed optimization-based algorithm in the HEMS instead of a simple rule-based algorithm under different pricing schemes. [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]

Abstract: Energy use attributed to residential buildings makes up nearly 16% of the total energy consumption in Mexico, yet a larger portion of the Mexican Official Standards for Energy (NOM-ENER) are specific to the commercial building sector which only makes up 3% of the total energy consumption in Mexico. Furthermore the NOM-ENER standards related to residential buildings are developed through a component based approach where energy efficiency guidelines are outlined for individual pieces of equipment with no interactions between these components taken into consideration. In this paper, a holistic and integrative energy analysis approach is considered to improve energy efficiency in residential buildings. Specifically, the study presented in this paper investigates the interactions between various energy efficiency measures and thermal comfort measures for residential buildings in Salamanca, Guanajuato using detailed simulation and optimization procedures. The results from the optimization analysis suggest a combination of improved appliance efficiencies, increased levels of roof and wall thermal insulation and improved water heating system efficiencies is required to achieve a minimum cost solution which results in nearly 52% annual energy savings for new homes. [Copyright &y& Elsevier]

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]

Abstract: Most air handling units (AHUs) in commercial buildings have an air economizer cycle for free cooling under certain outside air conditions. During the economizer cycle, the outside air and return air dampers are modulated to seek supply air temperature at its setpoint. The supply air temperature is typically set at 55F (13°C) to control humidity in the space. However, dehumidification is not necessary when the outside air is dry. Meanwhile, the space may have less cooling load due to envelope heat loss and/or occupant schedule changes. These facts provide an opportunity to use higher supply air temperature to reduce or eliminate mechanical cooling and terminal box reheat. On the contrary, a higher supply air temperature requires increased air flow as well as fan power. Therefore, an optimization question is formed, through which an optimal supply air temperature is identified to minimize total energy consumption. In this paper a steady-state energy consumption model is established for AHU systems under the economizer, and then an analytical optimization method is used to seek an optimal supply air temperature setpoint to minimize the energy cost. This paper presents AHU system energy modeling, supply air temperature optimization, simulated energy savings, and control sequence development. [Copyright &y& Elsevier]

Abstract: This paper presents the optimization of chillers operating using artificial neural networks and genetic algorithms. For the needs of generating chiller models, an artificial neural network was used, trained with data collected from an actual chiller. For that purpose the basic characteristics of artificial neural networks are shown as well as the process of making specific chiller models used for testing the results of application of the genetic algorithm in usage optimization. The optimal criteria with the shown steps for the use of the genetic algorithm and optimization results is also displayed in the paper. The results of use of artificial intelligence methods in optimization of chiller operation are verified through an actual office building model created in the simulation software EnergyPlus and through a series of experiments on an actual office building, equipped with a modern integrated BMS. [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]

Abstract: This paper presents an investigation of how Model Predictive Control (MPC) and weather predictions can increase the energy efficiency in Integrated Room Automation (IRA) while respecting occupant comfort. IRA deals with the simultaneous control of heating, ventilation and air conditioning (HVAC) as well as blind positioning and electric lighting of a building zone such that the room temperature as well as CO2 and luminance levels stay within given comfort ranges. MPC is an advanced control technique which, when applied to buildings, employs a model of the building dynamics and solves an optimization problem to determine the optimal control inputs. In this paper it is reported on the development and analysis of a Stochastic Model Predictive Control (SMPC) strategy for building climate control that takes into account the uncertainty due to the use of weather predictions. As first step the potential of MPC was assessed by means of a large-scale factorial simulation study that considered different types of buildings and HVAC systems at four representative European sites. Then for selected representative cases the control performance of SMPC, the impact of the accuracy of weather predictions, as well as the tunability of SMPC were investigated. The findings suggest that SMPC outperforms current control practice. [Copyright &y& Elsevier]

Abstract: This paper proposes a formulation of the global energy management problem of dwellings, which consists in a dynamic predictive control system able to generate optimized controls taking into account the model of the concerned dwellings, i.e. homes or offices. It focuses on the adjustment of the electric energy consumption and production in order to maximize energy usage efficiency, which is seen as a compromise between energy cost and overall comfort. To reach this objective, the concept of service is introduced: basically, a service participates to the comfort and may consume energy. The available flexibilities of the services provided by domestic appliances are used to compute anticipative optimal plans for appliance controls based on a mixed integer linear programming (MILP) algorithm. A reactive mechanism based on a list algorithm is added to face unforeseen events. The paper focuses on the computation of the anticipative plans. Different MILP models of common services are proposed. Application examples are given. [Copyright &y& Elsevier]

Abstract: Optimizing the configuration and operation of a CHP system for a whole year becomes a computationally demanding task when, for example, integer variables are used to model the status (on/off) of different pieces of equipment. The reason is that a discrete optimization problem is fundamentally an enumerative problem, featuring that the number of possible solutions grows exponentially with the number of integer variables. This computational difficulty is known as the curse of dimensionality, and severely limits the chances to use mixed integer programming methods to design CHP systems. To work out this problem, this paper presents a new and unambiguous method to reduce a full year of demand data to a few representative days that adequately preserve significant characteristics such as the peak demands, the demand duration curves, and the temporal inter-relationship between the different types of demands (power, heating, and cooling). Days are selected using a partitional clustering method known as the k-medoids method, and their ability to resemble the original data is tested by means of two quality indexes and a calendar visual inspection. Two case studies are discussed for the completeness of the paper, showing how the method and the quality indexes can be used in practice. [Copyright &y& Elsevier]

• Cost-effective and sustainable energy systems retrofit for buildings. • 2nd life Li-Ion batteries in polygeneration systems for buildings. • To cut off the natural gas consumption at affordable cost. The building sector represents around one-third of the energy related to the EU CO 2 eq emissions, which makes it a crucial sector for achieving the EU's energy and environmental goals. Thus, the EU has established a legislative framework to foster, among others, the modernisation of the existing building stock through a better energy system integration. In this sense, bearing in mind the needs of energy system retrofit of the public buildings in Spain, this paper carried out a thorough analysis of different trade-off solutions obtained from the multiobjective optimization of a polygeneration system for the TR5 building of the Polytechnic University of Catalunya. The results highlight the selection of PV panels, cogeneration modules and 2 nd life Li-Ion batteries, among others, to achieve cost-effective and sustainable energy systems. By covering the available area, 2000 m 2 , the PV panels attend about 23% of the electricity required for the building. On the other hand, considering the current geopolitical tensions, it presents a potential configuration that allows to cut off the natural gas consumption reducing about 6% the current cost. The study was carried out by using a Mixed Integer Linear Programming model maximizing the Net Present Value of the project considering the environmental impact. [ABSTRACT FROM AUTHOR]

Temperature and humidity independent control (THIC) systems demonstrate promising energy-saving potential compared with the conventional air conditioning systems. One type of THIC systems is proposed to use dual cooling sources with different temperatures since they could be produced easily by traditional vapor compression refrigeration chillers. This paper investigates the applicable condition of the THIC system based on dual cooling sources (DCSTHIC system). It is found that the DCSTHIC system has limited conditions (such as scopes of the heat moisture ratio and the fresh air rate) when applied, otherwise the indoor air temperature and humidity can’t be fulfilled, and further available scope of the heat moisture ratio realized by the DCSTHIC system is disclosed for air conditioned spaces. The minimum realized heat moisture ratio depends on the temperature of low-temperature cooling sources ( t LCS ). A parametric study is also conducted to analyze effects on the energy efficiency of DCSTHIC systems. These parameters include the heat moisture ratio ( ε ) and temperatures of dual cooling sources. Results show that DCSTHIC systems show more significant energy-saving potential in the air conditioned spaces with higher ε . The system COP improvement ranges from 3.1% to 17.5% compared with conventional air conditioning systems when the heat moisture ratio changes from 8000 kJ/kg to 15,000 kJ/kg. This study helps to guide the design and optimization of DCSTHIC systems. [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]

Effective demand responsiveness (DR) is crucial to the stability of the electrical grid. The increasing penetration of renewable energy sources demands higher load variation adaptability. Therefore, consumer-side flexibility is required for responding to abrupt DR signals. Real-time pricing (RTP) offer a direct approach for continually communicating DR signals. RTP has shown effectiveness in residential applications, however, its implications are impaired in industrial buildings which are less price-elastic due to stresses imposed by just in time (JIT) manufacturing and market competition. In this paper, we propose an instantaneous demand control methodology for industrial and commercial buildings, where the DR action is continually updated as new DR signals are received. We utilize the hour-ahead RTP (RTP-HA) tariffs and the demand shifting concept. The instantaneous approach is independent of price prediction uncertainty and scheduling approaches. The controller algorithm is converted to a linear optimization problem which is solved optimally and saves computational time, making it practical for real-time use. The method is robust and verified using MATLAB/SIMULINK with actual, 1 week, data from eight industrial and commercial buildings in Florida. Results show modest reductions in consumers’ electricity bills while maintaining required comfort standards. Results also address the load synchronization problem associated with RTP. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation method of operation performance of a HVAC system based on the ideal operation level of system. The HVAC system is divided into three subsystems and the ideal exergy flow model of each subsystem is set up based on exergy analysis. The ideal operation level of subsystem is defined and obtained by minimizing avoidable part of exergy destruction caused by the control condition. Based on the ideal operation levels of three HVAC subsystems, the ideal operation level of the HVAC system is set up. The evaluation index-improvement potential ( IP sys ) is defined to evaluate the operation performance of the HVAC system based on the ideal operation level. Five control strategies are applied in the HVAC system to validate the evaluation index and the evaluation method. The results show that the daily IP sys of using the global optimization control strategy on a typical day is 0.115 and is the smallest among those five strategies. It implies that the operation performance of the HVAC system with the global optimization control strategy is the closest to the idea operation level. The evaluation results indicate that the evaluation method not only can evaluate the operation performance of the HVAC system, but also can give the direction for improving the operation performance of the HVAC system. [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]

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]

This paper focuses on optimization of vertical ground coupled heat pump (VGCHP) system. This optimization is realized by examining effects of parameters on the system. Thus, experimental studies have been conducted under varying depth of boreholes and temperatures of condenser and evaporator. Then, the Taguchi method is applied to these parameters to optimize coefficient of performance (COP) of VGCHP system. Also, analysis of variance (ANOVA) and signal/noise (S/N) ratio are used for evaluation of experiment results. It is found that depth of borehole is the most significant parameter, affecting the COP by 67.77%. [ABSTRACT FROM AUTHOR]

The cooling load prediction of heating, ventilating and air-conditioning (HVAC) systems in office buildings is fundamental work for optimizing the operation of HVAC systems. In this paper, an improved multivariable linear regression model is proposed to predict the daily mean cooling load of office buildings in which three main measures, including the principal component analysis (PCA) of meteorological factors, cumulative effect of high temperature (CEHT) and dynamic two-step correction, are used to improve prediction accuracy. The site measured cooling load of two office buildings in Tianjin is used to validate the model and evaluate the prediction accuracy. Meanwhile, four contrast models with one or two of the three measures are also built. A comparison among the models proves that a combination of the three measures could effectively improve the prediction accuracy. The predicted load of the proposed model has acceptable agreement with actual load, where the mean absolute relative error is less than 8%. [ABSTRACT FROM AUTHOR]

The increasing popularity of smart energy systems has led to a gradual increase in the importance of thermal energy storage (TES) technology. Thus, the control strategy employed to efficiently take advantage of TES is expected to be very important. In other words, the time schedule, the particular components to be activated, and the amount of charging/discharging have to be appropriately determined. To date, a number of studies have investigated the optimization of TES operations by using optimization techniques. Current methods being used to achieve optimal TES operation are reviewed in this paper. [ABSTRACT FROM AUTHOR]

A multi-objective fire safety and sustainability screening tool for specifying insulation materials has been developed. This paper discusses a methodology for balancing competing requirements by evaluating the thermal resistance, fire performance, sustainability, cost, acoustic damping, and durability objectives of various insulating materials through implementation of a weighted mean. Each variable is normalized and then weighted according to the emphasis placed on each objective, using experimental data for the relevant material property. Two control scenarios and four weighting scenarios are presented. The first control scenario excludes both the fire performance and sustainability objectives in the material evaluations. The second control scenario introduces sustainability as an objective, but still excludes fire performance. The four weighting scenarios each emphasize a different area of consideration: cost, thermal resistance, sustainability, or fire performance. Materials considered are cellulose, fiberglass, rockwool, polyurethane, and polystyrene. Results of this analysis rank the materials in order of desirability and provide a method to reorder this ranking based on the priority assigned to each objective. For the four weighting scenarios presented, rockwool was consistently ranked as the best performer, while extruded polystyrene was typically the weakest. However, in the first control scenario, closed-cell polyurethane performed best and cellulose performed worst. [ABSTRACT FROM AUTHOR]

In many modern buildings, different monitoring and control systems are used, but majority of them are not directly connected with the energy consumption optimisation. This paper presents a concept of an integrated performance monitoring system for promotion of energy awareness in buildings. The reference architecture of the envisioned system evolved from the traditional management structure, plan-do-check-act model. Traditional plan-do-check-act model has been upgraded with the integration of prediction engine, knowledge repository and modern scientific approaches for supporting energy awareness including early warning, optimisation and supervision. The proposed concept is constituted upon three modules: (1) data acquisition and processing, (2) knowledge repository, (3) performance monitoring system including prediction engine, consumption modelling, emissions calculation and decision support. The concept of three modules evolved from the idea to enrich existing solutions with the awareness about possible future abnormal situations and to enable preventive actions. Presented case study represents a repeatable application of the energy cost centres based modelling of energy consumption in educational and research buildings by using neuro-fuzzy modelling approach. Initial testing results are indicating the potential of electricity savings up to 15% enabled by visualisation and awareness about electricity consumption. [ABSTRACT FROM AUTHOR]

The aim of the present paper is to analyze a building optimization and control (BOC) algorithm which is implemented in the existing building energy management system (BEMS) of the Saint George Hospital in Chania, Greece. The developed algorithm consists of predicted models for outdoor/indoor air temperature using artificial neural networks, multi-step optimization using genetic algorithms and Real Time control using fuzzy techniques. The algorithm is developed in Matlab™ environment and is implemented to the existing BEMS of the Hospital, using a specialized web-based energy management and control system (Web-EMCS). The implementation of the BOC algorithm is realized by developing a “.net assembly” code, which interconnects the Web-EMCS with the existing conventional hospital's BEMS, without the need of Matlab™. The annual primary energy efficiency achieved is almost 36%. [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]

In order to optimize the global energy performance of a ground source heat pump system, special attention needs to be paid to the auxiliaries as they stand for a considerable part of the total energy consumption. A new in situ experimental methodology based on the frequency variation of the water circulation pumps in order to optimize the energy performance of the system was previously published by the authors for a ground source heat pump system using a single stage heat pump with ON/OFF regulation. The original single stage heat pump was recently replaced with a multistage unit consisting of two compressors of the same capacity working in tandem. A new experimental campaign was carried out and a new study was performed in order to adapt the in situ optimization methodology to the performance of the tandem compressors unit, and, by extension, to the multistage case. This paper presents the in situ optimization methodology for the water circulation pumps frequency adapted for multistage ground source heat pump systems. Results show that energy savings up to 32% can be obtained by applying this optimization methodology. [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]