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.
Building heating, ventilation, and air conditioning (HVAC) systems consume large amounts of energy, and precise energy prediction is necessary for developing various energy-efficiency strategies. ...Energy prediction using data-driven models has received increasing attention in recent years. Typically, two types of driven models are used for building energy prediction: sequential and parallel predictive models. The latter uses the historical energy of the target building as training data to predict future energy consumption. However, for newly built buildings or buildings without historical data records, the energy can be estimated using the parallel model, which employs the energy data of similar buildings as training data. The second predictive model is seldom studied because the model input feature is difficult to identify and collect. Herein, we propose a novel key-variable-based parallel HVAC energy predictive model. This model has informative input features (including meteorological data, occupancy activity, and key variables representing building and system characteristics) and a simple architecture. A general key-variable screening toolkit which was more versatile and flexible than present parametric analysis tools was developed to facilitate the selection of key variables for the parallel HVAC energy predictive model. A case study is conducted to screen the key variables of hotel buildings in eastern China, based on which a parallel chiller energy predictive model is trained and tested. The average cross-test error measured in terms of the coefficient of variation of the root mean square error (CV-RMSE) and normalized mean bias error (NMBE) of the parallel chiller energy predictive model is approximately 16% and 8.3%, which is acceptable for energy prediction without using historical energy data of the target building.
Although computer technologies have greatly advanced in recent years and help engineers improve work efficiency, the heating, ventilation, and air conditioning (HVAC) design process is still very ...time-consuming. In this paper, we propose a conceptual framework for automating the entire design process to replace current human-based HVAC design procedures. This framework includes the following automated processes: building information modeling (BIM) simplification, building energy modeling (BEM) generation & load calculation, HVAC system topology generation & equipment sizing, and system diagram generation. In this study, we analyze the importance of each process and possible ways to implement them using software. Then, we use a case study to test the automated design procedure and illustrate the feasibility of the new automated design approach. The purpose of this study is to simplify the steps in the traditional rule-based HVAC system design process by introducing artificial intelligence (AI) technology based on the traditional computer-aided design (CAD) process. Experimental results show that the automatic processes are feasible, compared with the traditional design process can effectively shorten the design time from 23.37 working hours to nearly 1 hour, and improve the efficiency.
•Discussed cross-building energy prediction in a limited-data context.•Compared different methods in a same case via a competition.•Identified hybrid strategies for hybrid building energy prediction ...models.•Discussed the shortcomings and suggestions for the data preparation process.•Highlighted the importance of data selection for accurate cross-building prediction.
With the evolution of new energy and carbon trading systems, it is important to accurately predict building energy consumption to help energy arrangements. Additionally, the widespread use of smart meters has introduced a new data context for building energy prediction. Building energy prediction techniques need improvement but the ideas of various new prediction methods are still on the way and have not yet been compared and tested side-by-side in the reported studies. Thus, we held a competition called ‘Energy Detective’. To investigate the status quo of the current prediction techniques, we designed a representative prediction case: cross-building prediction with limited physical parameters and historical data. A total of 195 participants formed 89 teams to participate in the competition. This paper describes the models presented in the competition. By analysing the methods and results, we identified strategies for the future development of energy prediction in hybrid modelling and data-driven modelling. For hybrid modelling, we discuss the basic strategies for hybrid models and suggest that more hybrid models can be developed by combining a wide variety of individual models in sequence or parallel or via feedback methods to achieve accurate and interpretable models. For data-driven modelling, we analyse and discuss the areas of improvement for the current data-driven workflow and suggest that processes other than model application are also important and should be carefully considered. Considering the increasing amount of data available for prediction, we discuss the shortcomings and suggestions for improving the current data preparation process. We recommend comprehensive consideration of the anomaly types in data pre-processing and a focus on feature engineering for higher accuracy and model interpretability, while emphasising the vital role of data selection in cross-building energy prediction.
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 framework model for electricity flexibility quantification was proposed.•Electricity flexibilities of thermal mass, HVAC and appliances were quantified.•Some easily obtainable parameters are required in the proposed quantification model.•An office building was analyzed to present the electricity flexibility in DR.
•Review of feature engineering research for HVAC energy forecasting models.•A novel feature engineering method for exploring informative features.•An easy-to-use, high-accuracy toolkit for demand ...response baseline calculation.•Comparative tests verify the stability and accuracy of this energy prediction toolkit.•The average CV-RMSE of the target models for hourly energy prediction is <8%.
The peak load caused by heating, ventilation, and air-conditioning (HVAC) systems is one of the main control targets of a demand response (DR) program. One key issue related to DR is the baseline energy consumption forecasting based on which the DR strategies and performance can be evaluated. Data-driven models, as a promising method for HVAC energy prediction, have been widely studied. But most existing researches have focused on developing complicated algorithms rather than exploring informative features. In this study, a comprehensive review of feature engineering for HVAC energy prediction model development is presented. A novel feature engineering method is roposed. Besides, an easy-to-use, high-accuracy HVAC energy forecasting toolkit that is applicable to datasets of various granularities is developed. This toolkit uses easily available meteorological parameters and raw historical energy data as inputs, on which it performs data preprocessing, feature extension, and integrated optimization, thereby producing the predicted data. By employing a novel feature extension strategy and integrated optimization of feature selection and hyperparameter tuning, this toolkit performs capably in terms of prediction accuracy and stability. The results of a comparative experiment conducted on large-scale data verify that the average forecasting error (measured in terms of the coefficient of variation of the root mean square error) is <8%.
•Energy flexibility in demand response building is experimentally investigated.•Passive building thermal mass and active energy storage systems are coupled.•Pre-cooling and temperature reset are ...considered to assess energy flexibility.•Short-term (0.5 h) and intermediate-term (2 h) demand response are achieved.
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.
Traditional HVAC design process contains a large amount of time-consuming work such as load calculation for each space, hydraulic calculation to determine duct size and careful duct layout ...arrangement to avoid collision. Computer is more suitable for such kind of "dumb" work with higher efficiency and less fault. Also present building design work is a less integrated process where engineers of each domain focus on their own work. This often result in unconformity and poor quality with limited project time. IFC is a neutral platform, open file format specification to facilitate interoperability in the architecture, engineering and construction. In this paper, we propose a general framework of an IFC based integrated semi-automated design tool for central HVAC system design. It contains four main modules corresponding to specific design procedures. Human is allowed to make decision during the design process for more flexibility.
Shading system plays a significant role in reducing building energy demand. To analyse the performance of a shading system, traditional method is either conducting experimental tests for solar heat ...gain coefficient (SHGC) or through detailed energy simulation for energy saving during specific period. But no simulation tool is able to accomplish the two objectives at the same time, and the latter is always too detailed and cumbersome with traditional simulation tools. To help architects analyse the shading system in a more comprehensive and simple way, a fast simulation programme—ShadingPlus—is proposed and developed in this work. With EnergyPlus as its core simulation engine, ShadingPlus applies an optimal methodology to calculate SHGC. Moreover, annual energy saving calculation is also available with ShadingPlus which reflects shading system in a more realistic way. It is expected that the analysis for shading system can be greatly simplified using this tool. Case studies are also given to illustrate the way ShadingPlus works.
PDF
