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Building thermal mass control has great potentials in saving energy consumption and cost. Optimal control schemes are able to utilize passive thermal mass storage to shift the cooling load from peak hours to off-peak hours to reduce energy costs. As such, this paper explores the idea of model predictive control for building thermal mass control. Specifically, this paper presents a study of developing and evaluating a multi-objective optimization based model predictive control framework for demand response oriented building thermal mass control. This multi-objective optimization framework takes both energy cost and thermal comfort into consideration simultaneously. In this study, the developed model predictive control framework has been applied in six commercial buildings at Boston, Chicago, and Miami, under typical summer weather conditions. Time-of-use electricity prices from these three locations are used to calculate the cooling and reheating energy costs. Pareto curves for optimal temperature setpoints under different thermal comfort requirements are calculated to show the trade-off between the cost saving and thermal comfort maintaining. Comparing with a typical “night setback” operation scheme, this model predictive control schemes are able to save energy costs from 20% to 60% at these three locations under different weather and energy pricing conditions. In addition, the Pareto curves also show that the energy cost saving potentials are highly dependent on the thermal comfort requirements, weather conditions, utility rate structures, and the building constructions. [ABSTRACT FROM AUTHOR]

Energy efficiency is one of the main requirements that buildings must satisfy nowadays, both when designing new buildings or when major renovation takes place. The main issues that need to be considered in designing energy efficient buildings are: reduction of energy consumption generated from fossil fuels and reduction of environment pollution. Building envelope is the most responsible architectural element of the building that impacts thermal comfort and energy balance. Besides thermal properties of the envelope, equipment for heating, cooling and lighting influence largely on energy consumption in buildings. From the design point of view, the analysis of various facade scenarios, in terms of their contribution to reducing energy consumption, is crucial and necessary for each specific case and climate. This paper presents the estimation of energy performances of different scenarios of the hypothetical models of façade design, in case of an office building in Belgrade climate conditions. For the analysis, the model of the typical office was created and three various facade concepts were applied. Methodological approach entails three steps: design of different facade concepts - models, numerical energy simulations of the models and comparison of the results. For each of the three shading concepts, the following scenarios have been created: the basic scenario without shadings, and three scenarios of different facade design solutions related to types of shading devices. Assessment of the scenarios includes consideration of energy demands for heating and cooling, reduction of energy consumption for cooling by implementation of different shading devices and comparison of the results. The results of the simulations show how much the various alternatives of shadings contribute to the reduction of total energy demands, and how much the application of shadings affects the reduction of environmental pollution. The design method, as well as the results, can generally be applicable for the design of new and renovation of existing office buildings, both in Belgrade and in places with similar climatic conditions. • Buildings with glazed envelope on the southern facades have large window heat gains. • Shadings contribute to the reduction of energy demand in glazed envelope buildings. • Horizontal shadings are the most efficient when placed in the upper zone of window. • Vertical louvres on south facade are the most efficient when oriented west or south. • Combination of horizontal and vertical shadings shows the best energy reduction results. [ABSTRACT FROM AUTHOR]

Abstract In metropolitan areas populated with commercial buildings, electric power supply is stringent especially during business hours. Demand side management using battery is a promising solution to mitigate peak demands, however long payback time creates barriers for large scale adoption. In this paper, we have developed a design phase battery life-cycle cost assessment tool and a runtime controller for the building owners, taking into account the degradation of battery. In the design phase, perfect knowledge on building load profile is assumed to estimate ideal payback time. In runtime, stochastic programming and load predictions are applied to address the uncertainties in loads for producing optimal battery operation. For validation, we have performed numerical experiments using the real-life tariff model serves New York City, Zn/MnO 2 battery, and state-of-the-art building simulation tool. Experimental results shows a small gap between design phase assessment and runtime control. To further examine the proposed methods, we have applied the same tariff model and performed numerical experiments on nine weather zones and three types of commercial buildings. On contrary to the common practice of shallow discharging battery for preventing phenomenal degradation, experimental results show promising payback time achieved by optimally deep discharge a battery. Highlights • Develops a battery life-cycle assessment tool that estimates ideal payback time. • Develops a stochastic runtime controller that addresses uncertainties in loads. • Considers battery degradation in both life-cycle assessment and runtime control. • Shows a small gap between the ideal and the runtime battery payback time. • Demonstrates promising payback time on different buildings and weather zones. [ABSTRACT FROM AUTHOR]

Projections of building electricity consumption are used in multiple fields and for a variety of purposes, from energy utility investment decisions to global climate assessments. Existing approaches to modeling building electricity consumption span a range of structural methodologies, spatial resolutions, and temporal scales, potentially leading to divergent projections. This paper compares how two models with different structures and resolutions respond to a common set of population and climate drivers in the western U.S. The BEND model simulates hourly residential and commercial building electricity consumption at the county scale by weighting the results of simulations of thousands of representative buildings. In contrast, the projected electricity demand in GCAM-USA is determined for each state, year, sector (residential/commercial), and service (e.g., heating, cooling, and others), based on population, income, technology, energy prices, and average annual climate. This paper aggregates the two models' results to a common resolution: annual residential and commercial building electricity consumption by state. Both models show similar responses to future projected population change and climate change, with population change having the larger impact between the two. Differences are primarily due to how the models capture changes in the aggregate energy efficiency of the building stock as it evolves over time. • Models of building electricity consumption are methodologically diverse. • Two models of building electricity using very different approaches are compared. • The models mostly agree in their projections of building electricity consumption. • How the efficiency of buildings evolves drives disagreement between the models. [ABSTRACT FROM AUTHOR]

This paper aims to develop a decision-making tool to minimize large commercial buildings' Heating, Ventilation, Air-conditioning, and Cooling (HVAC) system environmental footprints. We modeled a terminal of an airport as a case study constituting hot water boilers, chillers, and air handling units, which forms the initial HVAC system, in EnergyPlus. The case study model is validated with monthly energy consumption data. Then, the decision-making system is applied on the retrofitted hybrid HVAC system resulted by combining Building Integrated Photovoltaic (BIPV) system, Combined Cooling, Heating and Power (CCHP) unit, and existing HVAC system. The decision-making system is developed based on three kinds of optimized controllers including Model Predictive Controller (MPC), Fuzzy Sliding Mode Control (FSMC), and relay controller (Initial controller of HVAC system). The optimization is designed based on reducing CO2 emission as well as providing comfort temperature. We found that retrofitting by hybrid system reduces emission by 90% compared with the initial system and applying intelligent controllers including MPC and FSMC, improve it to 95% CO2 emission reduction. While initial system provides comfort temperature 85% of the simulation period, retrofitted HVAC system controlled by MPC or FSMC sets indoor temperature 96% of 1-year simulation. • The distributed controllers tunes hybrid HVAC systems in an airport terminal. • The controllers' performances are evaluated in reducing environmental footprints. • The methods' efficacies are evaluated in operational cost & zones comfort condition. • The controllers' performance is analyzed by applying the 1 sample year condition. • The reliability of case study model is confirmed based on building utility data. [ABSTRACT FROM AUTHOR]

Imbalances in energy demand and supply related to increased use of renewable energy sources will eventually cause problems with the reliability of the power grid. The reliability of the grid requires ancillary services for power generation, as well as flexible consumption via demand response. In this paper, a multi agent-based distributed control strategy is proposed for commercial buildings to coordinate the flexible building loads with thermal energy storage systems (TESS). The distributed control uses the market mechanisms and price/incentive signals to engage self-interested responsive loads to provide services to the electrical power grid and managing buildings' electricity consumption. The proposed strategy was validated in a simulation environment to provide two grid service use cases. One is to limit the peak demand of building in which a market clearing strategy with respect to the demand limit is proposed. The other one is to perform price responsive control, in which the peak demand of building responds to the time of use price signals. The peak load reduction is calculated as a percentage of baseline peak load measured in the baseline for the same day. Energy consumption cost saving is defined as the percentage change from the baseline over the entire 5-day simulation. The results show that: (1) the demand limit control can reduce by up to 14% of building energy cost and 13% of peak demand and (2) the price response control can reduce by up to 16% of building energy cost. Overall, the results demonstrate that the distributed control with TESS proposed in this study is effective in reducing electricity use and peak building electric demand. • A market based control is proposed for a demand limiting and a price response. • An optimal control is developed for TESS when pricing electric rates are available. • A co-simulation capability that couples VOLTTRONTM™ to EnergyPlus is employed. • A market based control with TESS is demonstrated for two grid service use cases. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a model to minimize life-cycle costs of meeting the energy demand (power, heating, cooling) of a commercial building by integrating cogeneration, solar and conventional energy sources. The possibility to export the onsite produced power under a given legislative framework is also considered. The output of the model is twofold: optimal investment planning and optimal operating strategies of the energy systems. Results of the model are presented analyzing the combination of each individual cogeneration technology with other energy systems. The analysis shows varied operating strategies and output levels among cogeneration technologies and the energy systems coupled with them. The cost-effectiveness of solar and cogeneration technologies and the application of the model in long term planning for commercial building sector in Portugal are also discussed. [Copyright &y& Elsevier]

This paper proposes an optimal energy management strategy (EMS) for DC microgrid. The studied system presents a commercial building power system that combines a photovoltaic array (PV), fuel cell (FC), a battery storage system and a bidirectional DC/AC grid converter. The integration of multiple power sources like renewables leads to techno-economical challenges including power quality, stability, fuel consumption, and efficiency. The proposed EMS is based on the salp swarm algorithm (SSA). This algorithm has been implemented because of considerable advantages such as its convergence properties and its reduced computing complexity. The step-by-step design of the proposed method is detailed. Then HIL tests are performed to validate the proposed EMS performances. The performance of the proposed EMS is compared with the state machine control strategy (SMC) in terms of system efficiency and fuel consumption where the obtained results prove the superiority of the proposed EMS (5.2 % fuel saving). Regarding the power quality, the proposed EMS is compared with EMS based PSO to investigate the optimizer influence, the obtained results confirm the ability of the proposed EMS to provide a superior power quality. Hence, the proposed EMS responds to the power systems challenges including power quality, fuel-saving and efficiency. • A DC microgrid based commercial building power system. • An optimized management strategy based salp swarm algorithm. • A comparative study with a state machine strategy. • The proposed EMS′ performance has been confirmed using processing in the loop (PIL). • The performance has been improved on both efficiency (72.18 %) and fuel saving (5.2 %). [ABSTRACT FROM AUTHOR]

Nowadays, due also to high hygrothermal comfort requirements, the energy consumption for heating/cooling of modern trains can reach 30% of the related overall electricity demand. Energy-saving of train Heating, Ventilation and Air Conditioning systems can be suitably assessed through dynamic simulation approaches. Specifically, the weather solicitation has to be dynamically accounted for by considering the actual moving train location and orientation. Through such methodology different innovative actions for energy efficiency, environmental impact reduction and comfort enhancement can be analysed by also assessing their economic feasibility. In this paper, a novel simulation tool for the complete performance analysis of trains was developed in TRNSYS environment. To show the capabilities of the considered approach, a novel case study referred to an existing medium-distance train operating in South Italy is presented. Heating/cooling loads and demands, and the related electricity requirements, are dynamically assessed for the standard and revamped train. Several energy saving actions are considered for the coupling between the envelope and the Heating, Ventilation and Air Conditioning systems enhancement. The obtained results return significant benefits in terms of energy saving, avoided CO 2 emissions and comfort. Paybacks depend on operating conditions. Useful design and operating criteria for trains manufacturers and users are provided. • Dynamic simulation for train HVAC system energy performance analyses using TRNSYS. • Customized weather data files for accounting real train locations and orientations. • Innovative energy efficiency solutions for trains with saving up to 6.0 MWh e /y. • Economic feasibility of energy efficiency solutions for trains. • Reduction up to 50% of the train environmental impact. [ABSTRACT FROM AUTHOR]

Many technical solutions have been developed to reduce buildings' energy consumption, but limited efforts have been made to adequately address the role or action of building occupants in this process. Our earlier investigations have shown that occupants play a significant role in buildings' energy consumption: It was shown that savings of up to 20% could be achieved by modifying occupant behavior thorough direct feedback and recommendations. Studying the role of occupants in building energy consumption requires an understanding of the interrelationships between climatic conditions; building characteristics; and building services and operation. This paper describes the development of a systematic procedure to provide building occupants with direct feedback and recommendations to help them take appropriate action to reduce building energy consumption. The procedure is geared toward developing a Reference Building (RB) (an energy-efficient building) for a specific given building. The RB is then compared against its given building to inform the occupants of the given building how they are using end-use loads and how they can improve them. The RB is generated using a data-mining approach, which involves clustering analysis and neural networks. The framework is based on clustering similar buildings by effects unrelated to occupant behavior. The buildings are then grouped based on their energy consumption, and those with lower consumption are combined to generate the RB. Performance evaluation is determined by comparison of a given building with an RB. This comparison provides feedback that can lead occupants to take appropriate measures (e.g., turning off unnecessary lights or heating, ventilation, and air conditioning (HVAC), etc.) to improve building energy performance. More accurate, scalable, and realistic results are achiveable through current methodology which is shown through comparison with existing literature. • A model is developed to create a reference building to evaluate the energy saving opportunities. • Developed data mining framework is applied and validated on a set of 76 buildings. • The methodology provides the occupants with insights regarding their energy savings. • The framework is generic and can be applied to a wide range of buildings. [ABSTRACT FROM AUTHOR]