Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy efficiency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most effective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy efficiency at a very early design stage. On the other hand,efficient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, artificial intelligence (AI) in general and machine learning (ML) techniques in specific terms have been proposed for forecasting of building energy consumption and performance. This paper provides a substantial review on the four main ML approaches including artificial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance.

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Machine learning for estimation of building energy consumption and performance: a review

Data-driven estimation of building energy consumption and GHG emissions using explainable artificial intelligence

The rapid depletion of natural sources of energy, coupled with increasing global population has triggered the emergence of various techniques and strategies for building energy consumption prediction. According to information from existing body of knowledge, this paper systematically brings to fore the application areas of building energy consumption prediction (i.e. well-established and emerging), the relationships between these areas and the ways in which authors integrate the current spate of techniques. Based on direct implications of buildings on global energy consumption and CO2 emissions, this information makes it possible to identify trends, strengths and limitations in this context, thereby enabling the centralisation of activities required for future studies. This study follows several well-documented guides for conducting logical reviews of primary articles concerning main topics of building energy consumption prediction within popular online databases. The definition of articles’ search keywords as well as inclusion/exemption factors were governed by a combination of principles stipulated by Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Procedures for Performing Systematic Reviews (PPSR). In comparison to existing review articles in the studied field, the current study is novel in the sense that it provides a very holistic view to building energy consumption prediction, thereby minimising the need to consult multiple individualised studies that are limited to specific techniques, data sets, regions or types of buildings. Another unique feature of this study is its interrelationship network of articles which depicts a quick glance at some of the most influential studies as well as underrepresented areas, thereby aiding research planning, future directions and cross-disciplinary collaborations.

Towards developing a systematic knowledge trend for building energy consumption prediction

The industrial sector consumes about one-third of global energy, making them a frequent target for energy use reduction. Variation in energy usage is observed with weather conditions, as space conditioning needs to change seasonally, and with production, energy-using equipment is directly tied to production rate. Previous models were based on engineering analyses of equipment and relied on site-specific details. Others consisted of single-variable regressors that did not capture all contributions to energy consumption. New modeling techniques could be applied to rectify these weaknesses. Applying data from 45 different manufacturing plants obtained from industrial energy audits, a supervised machine-learning model is developed to create a general predictor for industrial building energy consumption. The model uses features of air enthalpy, solar radiation, and wind speed to predict weather-dependency; motor, steam, and compressed air system parameters to capture support equipment contributions; and operating schedule, production rate, number of employees, and floor area to determine production-dependency. Results showed that a model that used a linear regressor over a transformed feature space could outperform a support vector machine and utilize features more representative of physical systems. Using informed parameters to build a reliable predictor will more accurately characterize a manufacturing facility's energy savings opportunities. • Industrial energy data from 45 manufacturing facilities are obtained from on-site energy audits. • A supervised ML model is developed to facilitate predicting industrial energy consumption. • Basis function model presented a grounding in the physical nature of industrial energy systems. • Enables energy engineers or plant personnel to construct a more descriptive energy model. 

Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters

As the level of greenhouse gas emissions increases, so does the importance of the energy performance of buildings (EPB). One of the main factors to measure EPB is a structure’s heating load (HL) and cooling load (CL). HLs and CLs depend on several variables, such as relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution. This research uses deep neural networks (DNNs) to forecast HLs and CLs for a variety of structures. The DNNs explored in this research include multi-layer perceptron (MLP) networks, and each of the models in this research was developed through extensive testing with a myriad number of layers, process elements, and other data preprocessing techniques. As a result, a DNN is shown to be an improvement for modeling HLs and CLs compared to traditional artificial neural network (ANN) models. In order to extract knowledge from a trained model, a post-processing technique, called sensitivity analysis (SA), was applied to the model that performed the best with respect to the selected goodness-of-fit metric on an independent set of testing data. There are two forms of SA—local and global methods—but both have the same purpose in terms of determining the significance of independent variables within a model. Local SA assumes inputs are independent of each other, while global SA does not. To further the contribution of the research presented within this article, the results of a global SA, called state-based sensitivity analysis (SBSA), are compared to the results obtained from a traditional local technique, called sensitivity analysis about the mean (SAAM). The results of the research demonstrate an improvement over existing conclusions found in literature, which is of particular interest to decision-makers and designers of building structures.

An Intelligent Model to Predict Energy Performances of Residential Buildings Based on Deep Neural Networks

Energy consumption of buildings is increasing steadily and occupying approximately 30-40% of total energy use. It is important to predict heating and cooling loads of a building in the initial stage of design to find out optimal solutions among various design options, as well as in the operating stage after the building has been completed for energy efficient operation. In this paper, an artificial neural network model has been developed to predict heating and cooling loads of a building based on simulation data for building energy performance. The input variables include relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution of a building, and the output variables include heating load (HL) and cooling load (CL) of the building. The simulation data used for training are the data published in the literature for various 768 residential buildings. ANNs have a merit in estimating output values for given input values satisfactorily, but it has a limitation in acquiring the effects of input variables individually. In order to analyze the effects of the variables, we used a method for design of experiment and conducted ANOVA analysis. The sensitivities of individual variables have been investigated and the most energy efficient solution has been estimated under given conditions. Discussions are included in the paper regarding the variables affecting heating load and cooling load significantly and the effects on heating and cooling loads of residential buildings.

Prediction and Analysis of Building Energy Efficiency Using Artificial Neural Network and Design of Experiments

Demand controlled ventilation has been proposed to improve indoor air quality and to save building energy consumption. It is important to estimate the number of occupants in a building in order to determine adequate ventilation airflow rates, especially when people are the major source of indoor contaminants such as in office buildings. In this paper, we investigate an occupancy estimation method using a dynamic neural network model based on carbon dioxide concentration in a space. CONTAM is used to generate CO 2 concentration data in a room with various scenarios of occupancy schedules and airflow rates based on realistic wind data. The method provides nearly perfect results for the scenarios with constant or continuously varying conditions of occupancy and airflow rates. The RMS error of the estimating method is greatly influenced by the characteristics of fluctuating airflow patterns and frequently varying occupancy. Further studies are needed to investigate more about the network model in detail so as to improve estimation accuracy for various scenarios.

Effect of Airflow and Occupancy Variations on the Estimation of the Number of Occupants in an Office Building

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Prediction and Analysis of Building Energy Efficiency Using Artificial Neural Network and Design of Experiments

Effect of Airflow and Occupancy Variations on the Estimation of the Number of Occupants in an Office Building