A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand: A review

• Published in: Energy and buildings Vol. 165; pp. 301 - 320
• Main Authors: Ahmad, Tanveer, Chen, Huanxin, Guo, Yabin, Wang, Jiangyu
• Format: Journal Article
• Language: English
• Published: Lausanne Elsevier B.V 15.04.2018; Elsevier BV
• Subjects: Artificial intelligence; Artificial neural networks; Benchmarking; Buildings; Clustering; Commercial buildings; Computer simulation; Construction methods; Data driven based approaches; Data mining; Data processing; Demand; Economic forecasting; Energy; Energy conservation; Energy consumption; Energy demand; Energy efficiency; Energy management; Energy mapping models; Energy usage; Fault detection; Fault diagnosis; Forecasting; Forecasting models; Learning algorithms; Machine learning; Mathematical models; Neural networks; Performance measurement; Prediction models; Residential energy; Scale (ratio); Simulation; Statistical and machine learning patterns; Support vector machines; Urban areas; Benchmarking; Data driven based approaches; Energy mapping models; Forecasting models; Statistical and machine learning patterns
• ISSN: 0378-7788; 1872-6178
• DOI: 10.1016/j.enbuild.2018.01.017

•The focus of this review is to target the data-driven and large-scale building energy forecasting techniques.•Data-driven based approaches are ANN, Clustering, SML, and SVM.•A variety of issues are explored which include: EMP, End-use of different building types; MLOG; and URS.•Benchmarking, energy-mapping, energy forecasting and energy profiling models are combined and analyzed.•The models are suitable with building energy simulation in rural and urban planning. Energy consumption models play an integral part in energy management and conservation, as it pertains to buildings. It can assist in evaluating building energy efficiency, in carrying out building commissioning, and in identifying and diagnosing building system faults. This review takes an in-depth look at energy-demand prediction models for buildings in that it delves into recent developments in building energy approaches used to predict energy usage. By enlisting current approaches to the modelling of buildings, methods for building energy simulations can be categorized into four level classes as follows: (i) data-driven approaches; (ii) physics-based approaches; (iii) large scale building energy forecasting approaches; and (iv) hybrid approaches. The focus of this review is to target the data-driven approach and large-scale building energy predicting-based approaches. Here the data driven approaches can be categorized by (1) artificial neural network based approaches; (2) clustering based approaches; (3) statistical and machine learning-based approaches; and (4) support vector machine based approaches. From there, the type of data-driven based approach is further grouped by (a) benchmarking models; (b) energy-mapping models; (c) energy forecasting models; and (d) energy profiling models. Large-scale building-energy prediction techniques is then categorized as follows: (1) white-box based approaches; (2) black-box based approaches, and (3) grey-box based approaches. The current study explores first-rate data-driven based approaches about building energy analysis for industrial, commercial, domestic, etc., within a rural and urban setting. This review paper is based on the necessity of identifying points of departure and research opportunities for urban and rural-level analyses of building level energy performance. A variety of issues are explored which include: energy performance metrics; end-use of different building types; multiple levels of granularity; and urban and rural scales. Each technique encompasses a variety of input information as well as varying calculations or simulation models along with furnishing contrasting outcomes that suggest a variety of usages. A thorough review of each technique is presented in this study. This review highlights strengths, shortcomings, and purpose of the methods of numerous data-mining based approaches. A comprehensive review of energy forecasting models that are specified in the literature part is also provided.