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Building energy systems, i.e. heating, ventilation, and air-conditioning (HVAC) systems, are essential for modern buildings. They provide a comfortable and healthy indoor environment. Design quality has significant impact on HVAC system efficiency. The typical building energy system design process involving several procedures is repetitive and time-consuming. It is often limited by the engineer's experience, capabilities, and time constraints; thus, the design in most cases barely satisfies building codes. In recent decades, computational intelligence (CI) has achieved substantial improvements in various fields. This paper presents a comprehensive review of using CI for HVAC system optimization design. Firstly, this paper analyzes seven procedures which constitute a typical HAVC system design process and finds that optimization problems encountered during design process can be divided into three categories: model estimation, decision making and uncertainty analysis. Then a brief introduction of CI techniques used to solve HVAC design optimization problems and detailed literature review of application examples are given. Though the design problem varies with each other, this paper outlines a typical workflow which is able to solve most HVAC optimization design problems. At last, a framework of an integrated HVAC automation and optimization design tool is proposed. The framework is developed based on building information modeling (BIM) and extracted typical design optimization workflow. It is able to connect various design stages by implementing structured information transfer between them and ultimately improve design efficiency and quality.

The energy performance and energy flexibility potential of a sorption-assisted water storage (SAWS) for a medium-scale residential building with eight apartments under the climatic condition of Potsdam, Germany were investigated. The SAWS system consists of a stratified water storage coupled to an adsorption heat pump (AHP). The driving heat source for the AHP was modeled as an ideal heater, which can be interpreted as an electric heater to serve as an off-peak storage heating system. Five representative partial heating loads of a building were investigated. Recharging situations of 2 h, 4 h, and 8 h with an interval of 2 h heater switching off were considered. Transient simulation results show that the 2 h heater switch off is feasible and does not significantly reduce the occupants’ thermal comfort in the four low heating load cases. However, for the highest heating load case, the load cannot be completely met. An average heating coefficient of performance of 1.33 for all the five load cases is achieved. Additionally, under the simple control scheme considered here, this SAWS system achieves high electricity flexibility by controlling the heater switch on and off schedule for a demand response program.

This paper classifies and discusses the energy flexibility improvement strategies for demand responsive control in grid-interactive buildings based on a comprehensive study of the literature. Both supply and demand sides are considered. The flexibility measures range from renewable energy such as photovoltaic cells (PV) and wind to heating, ventilation, and air conditioning (HVAC) systems, energy storage, building thermal mass, appliances, and occupant behaviors. Currently, owing to the highly developed smart appliances and sensing communication techniques, DR is considered as an essential measure for improving energy flexibility in buildings without much additional investment. With the help of advanced demand response (DR) control strategies and measures, buildings can become more flexible in terms of power demand from the power grid. In this way, buildings achieve a better ability to balance differences in energy supply and demand. Furthermore, a synergistic approach with various measures is advisable, e.g., the use of energy storage technologies with PV and passive DR methods. This paper summarizes the measures for improving the flexibility of commercial and residential buildings, and develops a systematic methodology framework to evaluate energy demand flexibility in buildings.

In recent years, with the rapid growth of electricity demand and the development of smart grids, demand-side management had an important role. As the penetration rate of distributed renewable energy generation in the grid increases, the diurnal peak of the net load demand curve in the urban area is offset by renewable energy sources such as photovoltaics and the peak load time gradually shifts from afternoon to evening. The peak electricity load of residential buildings usually occurs in the evening, which aggravates the power balance problem. To this end, this study proposes a demand response (DR) scheduling approach for residential buildings, aimed for four types of residential building loads: interruptible and deferrable loads, noninterruptible and deferrable loads, noninterruptible and nondeferrable loads, and air conditioning loads. Nondominated sorting genetic algorithm II is used as a multi-objective optimization algorithm to search for the minimal electricity cost and minimal inconvenience index. Finally, the ASHRAE 140 standard building is used as a case and the proposed scheduling approach is evaluated under two scenarios of working and nonworking days. The proposed scheduling approach can effectively shave the peak load to off-peak load time, reduce electricity bills, and meet the occupants’ comfort.

Traditional rural energy sources, such as straw, firewood, and coal are the main energy resources in developing countries; however, these resources are associated with low efficiency and can cause considerable air pollution. With the rapid development of renewable energy worldwide, existing energy frameworks in rural areas need to be urgently transformed. Developing a universal and reliable dynamic modeling framework for multi-source heat pumps remains an unresolved issue. To this end, this paper proposes an integrated energy system based on a solar-assisted ground source heat pump (SGSHP), which can facilitate the utilization of renewable energy, reduce the electricity consumption, and provide demand response (DR) resources in rural houses. A 150-m2 rural house on the outskirts of Shanghai, China was considered as a case building. A novel Dymola modeling framework of an SGSHP system was established, and the heating performance was analyzed. Considering the variation in the energy demand in winter, four practical operation modes were implemented to maximize energy efficiency. The results show that the coefficients of performance of the system are 4.2 and 3.5 with and without solar collectors, respectively. The contributions of solar energy, geothermal heat, and electricity in the system are 27%, 18%, and 55%, respectively. Moreover, approximately 79.6% of AC loads can be shaved during evening peak load time in our proposed energy system.

Electricity demand response (DR) improves the overall energy management efficiency and allows for the integration of large-scale renewable energy into the power grid through interactive management and control of the supply and demand sides. However, in China and other emerging countries (e.g., Japan and Australia) with DR programs, the DR market mechanism is not well-established. This is particularly the situation in countries where the state power system is not freely open to participate in the DR market. Previous research has proven that considerable DR resources are largely existing in the demand side; however, the dearth of a market mechanism hinders the development of DR. Hence, a feasible market mechanism is urgently required. In this study, first, the experiences in existing DR-developed markets where DR has been legally regarded as an equivalent electricity resource are investigated. Second, a pilot DR program in Shanghai representing emerging DR markets with a non-liberated electricity background is presented. Moreover, two market mechanisms are analyzed: the energy efficiency obligation mode and the electricity capacity trading mode. Finally, five feasible DR mechanisms—mandatory energy-saving, cap and trade, DR-tiered electricity tariff, price-based program, and economic and emergency DR—are proposed for China and other DR emerging countries.

Demand response is an efficient method to flatten the demand curves of end-use customers. This article studies the feasibility of an adsorption heat pump in the demand response of a residential building under winter operating conditions. Stratified storage is introduced into the stratified heat pump system to realize heat recovery, which is also used as a buffer energy source when power shortages occur. This article studies the performance of an adsorption heat pump when the system disconnects the external energy source to simulate the situation of a demand response event occurring through experiments. Moreover, the heating loads of a typical residential building are obtained from the simulations on EnergyPlus. Two day types are selected to evaluate the demand response performance of the system in different situations. The coolest day presents an extreme situation, and the design day represents a normal situation. The demand response potential of the adsorption heat pump is estimated by comparing the heat...

Heating, ventilation, and air conditioning (HVAC) systems, combined with the internal thermal mass of buildings, have been deemed to be promising means of providing demand response (DR) resources, particularly for buildings with active energy storage systems. DR resources, such as peak-load reduction potential, can provide grid-responsive support resulting in a high degree of grid involvement and high flexible electricity demand. In the DR field, the potential of HVAC load flexibility has been considered in buildings. In the future smart buildings, it is important to take advantage of demand-side resources to achieve real-time energy supply–demand balance sustainably. In this context, DR potential and characteristics of buildings play a pivotal role in DR programs. However, few studies have investigated the internal thermal mass’s heat release and DR characteristics of buildings. Thus, a systematic experiment is conducted to study the DR potential and characteristics of internal thermal mass and active storage systems. The DR resources include the passive cooling storage from furniture, building envelope and an active water storage tank. Two DR control strategies, including pre-cooling and temperature resetting, are analyzed in this study. The experimental results show that the strategies are effective for short-term (0.5 h) and intermediate-term (2 h) DR programs. For a long-term DR program, active energy storage technology such as a water storage tank is required to satisfy the occupant's comfort requirements. Hence, we conclude that passive thermal mass and active storage systems should be simultaneously considered in practical DR programs for better DR implementation.

Electric demand flexibility in buildings has been deemed to be a promising demand response resource, particularly for large commercial buildings, and it can provide grid-responsive support. A building with a higher electricity flexibility potential has a higher degree of involvement with the grid response. If the electricity flexibility potential of a building is known, building operators can properly alleviate peak loads and maximize economic benefits through precise control in demand response programs. Previously, there was no standard way to quantify electricity flexibility, and it was difficult to evaluate a given building without experiments and tests. Thus, a systematic approach is proposed to quantify building electricity flexibility. The flexibility contributions include building thermal mass; lights; heating, ventilation, and air conditioning (HVAC) systems, and occupant behaviors. This proposed model has been validated by the instantiation of an office building case on the Dymola platform. For a typical office building, the results show that the electricity flexibility resource not only comes from the HVAC system, but also thermal mass and occupant behavior to a large degree, and buildings with energy flexibility can cut down much of their load during peak load time without compromising on the occupant's comfort.

A building heating, ventilation, and air-conditioning (HVAC) system consumes large amounts of energy. Energy consumption prediction is an effective strategy for operation optimization and energy management in a building. The energy consumption of an HVAC system in a building is influenced by many factors, such as weather conditions, building usage, and thermal performance. However, it is impractical to consider all factors for predicting energy consumption. In this paper, a simplified data-driven model is proposed for predicting the energy consumption of an HVAC system in a building. A novel feature transformation method is introduced to select the most relevant features. Three input features (i.e., degree-day, day type, and month type) are finally adopted in this model. Compared to models developed in previous studies, this simplified model largely reduces the computation time and is easier to operate. The cross-validated root mean square error of this method for cooling energy prediction is less than 20%, indicating its suitability for use in engineering applications.