The difficulty in balancing energy supply and demand is increasing due to the growth of diversified and flexible building energy resources, particularly the rapid development of intermittent ...renewable energy being added into the power grid. The accuracy of building energy consumption prediction is of top priority for the electricity market management to ensure grid safety and reduce financial risks. The accuracy and speed of load prediction are fundamental prerequisites for different objectives such as long-term planning and short-term optimization of energy systems in buildings and the power grid. The past few decades have seen the impressive development of time series load forecasting models focusing on different domains and objectives. This paper presents an in-depth review and discussion of building energy prediction models. Three widely used prediction approaches, namely, building physical energy models (i.e., white box), data-driven models (i.e., black box), and hybrid models (i.e., grey box), were classified and introduced. The principles, advantages, limitations, and practical applications of each model were investigated. Based on this review, the research priorities and future directions in the domain of building energy prediction are highlighted. The conclusions drawn in this review could guide the future development of building energy prediction, and therefore facilitate the energy management and efficiency of buildings.
•A new SVR model to forecast the demand response baseline for office buildings.•Take temperature two hours before DR event can improve the forecasting accuracy.•The forecasting accuracy is better ...than other seven existing methods in DR programs.•The model is very generic and can be applied to a wide variety of office buildings.
Demand Response (DR) aims at improving the operation efficiency of power plants and grids, and it constitutes an effective means of reducing grid risk during peak periods to ensure the safety of power supplies. One key challenge related to DR is the calculation of load baselines. A fair and accurate baseline serves as useful information for resource planners and system operators who wish to implement DR programs. In the meantime, baseline calculation cannot be too complex, and in most cases, only weather data input is permitted. Inspired by the strong non-linear capabilities of Support Vector Regression (SVR), this paper proposes a new SVR forecasting model with the ambient temperature of two hours before DR event as input variables. We use electricity loads for four typical office buildings as sample data to test the method. After analyzing the model prediction results, we find that the SVR model offers a higher degree of prediction accuracy and stability in short-term load forecasting compared to the other seven traditional forecasting models.
Abstract
Accurate solar and wind generation forecasting along with high renewable energy penetration in power grids throughout the world are crucial to the days-ahead power scheduling of energy ...systems. It is difficult to precisely forecast on-site power generation due to the intermittency and fluctuation characteristics of solar and wind energy. Solar and wind generation data from on-site sources are beneficial for the development of data-driven forecasting models. In this paper, an open dataset consisting of data collected from on-site renewable energy stations, including six wind farms and eight solar stations in China, is provided. Over two years (2019–2020), power generation and weather-related data were collected at 15-minute intervals. The dataset was used in the Renewable Energy Generation Forecasting Competition hosted by the Chinese State Grid in 2021. The process of data collection, data processing, and potential applications are described. The use of this dataset is promising for the development of data-driven forecasting models for renewable energy generation and the optimization of electricity demand response (DR) programs for the power grid.
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.
•Three types of optimization problems are defined and corresponding solving methods are presented.•Detailed literature review of CI application in each procedure of HVAC design process is presented.•A typical workflow to solve HVAC design optimization problems is extracted.•Framework of an integrated HVAC automation and optimization design tool based on BIM and typical design optimization workflow is proposed.
Occupancy is one of the main factors affecting building energy consumption. The occupancy data, which refer to the occupancy number in this paper, has been widely used in the building simulation ...field. However, due to the stochastic nature of occupant behavior, it is hard to predict and measure how many people stay in a given building. The rapid development of mobile Internet technology provides an efficient and convenient option for occupancy detection. This paper proposes a concept of typical occupancy data (TOD), which are extracted from real-time occupancy data collected by mobile devices. K-means algorithm is employed to generate the TOD data through cluster analysis. An energy performance model of an office building is used as a case study to demonstrate the effectiveness of the TOD data.
With the rapid development of large-scale building energy monitoring platforms, it is of great significance to develop precise forecasting methods for buildings on a large scale to achieve better ...energy system design, system operation, energy management, and renewable energy integration in the grid. Traditionally, using all available historical data to train a data-driven model has been widely employed to ensure prediction performance because more historical information can be learned. However, this strategy may introduce more noise, especially for short-term load forecasting. Thus, this study proposes a novel approach for selectively utilizing building historical data to determine the amount of data that should be used to train the data-driven model. First, the CV(RMSE) curve of each building reflecting the relationship between training data length and forecasting accuracy is obtained using LightGBM. Second, clustering techniques such as k-means are used to identify buildings that are sensitive to the training data length based on CV(RMSE) curves. Finally, the optimal training data length for day-ahead forecasting is estimated for each building. The case study shows that approximately 20% of buildings in the Building Data Genome are labeled as length-sensitive buildings, and adopting appropriate training data lengths can reduce the prediction error of these buildings by up to 15%.
A cooling tower is an important guarantee for the proper operation of a solar system. To ensure proper operation of the system and to maintain high-efficiency points, the cooling tower must operate ...year-round. However, freezing is a common problem that degrades the performance of cooling towers in winter. For example, the air inlet forms hanging ice, which clogs the air path, and the coil in closed cooling towers freezes and cracks, leading to water leakage in the internal circulation. This has become an intractable problem that affects the safety and performance of cooling systems in winter. To address this problem, three methods of freeze protection for cooling towers are studied: (a) the dry and wet mixing operation method—the method of selecting heat exchangers under dry operation at different environments and inlet water temperatures is presented. The numerical experiment shows that the dry and wet mixing operation method can effectively avoid ice hanging on the air inlet. (b) The engineering plastic capillary mats method—its freeze protection characteristics, thermal performance, and economics are studied, and the experiment result is that polyethylene (PE) can meet the demands of freeze protection. (c) The antifreeze fluid method—the cooling capacity of the closed cooling towers with different concentrations of glycol antifreeze fluid is numerically studied by analyzing the heat transfer coefficient ratio, the air volume ratio, the heat dissipation ratio, and the flow rate ratio. The addition of glycol will reduce the cooling capacity of the closed cooling tower.
The penetration rates of intermittent renewable energies such as wind and solar energy have been increasing in power grids, often leading to a massive peak-to-valley difference in the net load ...demand, known as a “duck curve”. The power demand and supply should remain balanced in real-time, however, traditional power plants generally cannot output a large range of variable loads to balance the demand and supply, resulting in the overgeneration of solar and wind energy in the grid. Meanwhile, the power generation hours of the plant are forced to be curtailed, leading to a decrease in energy efficiency. Building demand response (DR) is considered as a promising technology for the collaborative control of energy supply and demand. Conventionally, building control approaches usually consider the minimization of total energy consumption as the optimization objective function; relatively few control methods have considered the balance of energy supply and demand under high renewable energy penetration. Thus, this paper proposes an innovative DR control approach that considers the energy flexibility of buildings. First, based on an energy flexibility quantification framework, the energy flexibility capacity of a typical office building is quantified; second, according to energy flexibility and a predictive net load demand curve of the grid, two DR control strategies are designed: rule-based and prediction-based DR control strategies. These two proposed control strategies are validated based on scenarios of heating, ventilation, and air conditioning (HVAC) systems with and without an energy storage tank. The results show that 24–55% of the building’s total load can be shifted from the peak load time to the valley load time, and that the duration is over 2 h, owing to the utilization of energy flexibility and the implementation of the proposed DR controls. The findings of this work are beneficial for smoothing the net load demand curve of a grid and improving the ability of a grid to adopt renewable energies.
As a means to adjust the temperature of the thermal zones in buildings, building thermal mass is regarded as one of the essential sources of energy flexibility. It is still challenging to quantify ...the energy flexibility of passive thermal mass, making it oppugning to use thermal mass for buildings’ demand response (DR). A method to accurately quantify the energy flexibility from heating, ventilation, and air conditioning systems (HVAC) is important for buildings to participate in DR projects. This paper proposes a novel data-driven model to quantify HVAC’s electrical demand under dynamic global temperature adjustment. The Markov chain is first used to implement an effective sampling method to produce a global temperature resetting schedule representing different temperature resetting. Next, EnergyPlus evaluates the HVAC electrical demand under the various temperature reset scenarios. In the end, the LightGBM algorithm is used to develop the data-driven model. Having validated the proposed model, the case study was conducted in a DOE reference office building for EnergyPlus. Results demonstrate that the Markov chain outperforms the probabilistic method when sampling temperature setpoint schedules. In the future, the proposed data-driven model can be used to evaluate the flexibility capacity of an energy management system in grid-integrated buildings.
An accurate and fast building load prediction model is critically important for guiding building energy system design, optimizing operational parameters, and balancing a power grid between energy ...supply and demand. A physics-based simulation tool is traditionally used to provide the building load demand; however, it is constrained by its complex model development process and requirement for engineering judgments. Machine learning algorithms (i.e., data-driven models) based on big data can bridge this gap. In this study, we used the massive energy data generated by a physics-based tool (EnergyPlus) to develop three data-driven models (i.e., LightGBM, random forest (RF), and long-short term memory (LSTM)) and compared their prediction performances. The physics-based models were developed using office prototype building models as baselines, and ranges were provided for selected key input parameters. Three different input feature dimensions (i.e., six-, nine-, and fifteen-input feature selections) were investigated, aiming to meet different demands for practical applications. We found that LightGBM significantly outperforms the RF and LSTM algorithms, not only with respect to prediction accuracy but also in regard to computation cost. The best prediction results show that the coefficient of variation of the root mean squared error (CVRMSE), squared correction coefficient (R2), and computation time are 5.25%, 0.9959, and 7.0 s for LightGBM, respectively, evidently better than the values for the algorithms based on RF (18.54%, 0.9482, and 44.6 s) and LSTM (22.06%, 0.9267, and 758.8 s). The findings demonstrate that a data-driven model is able to avoid the process of establishing a complicated physics-based model for predicting a building’s thermal load, with similar accuracy to that of a physics-based simulation tool.
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