A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings

Abstract

Buildings account for a significant portion of our overall energy usage and associated greenhouse gas emissions. With the increasing concerns regarding climate change, there are growing needs for energy reduction and increasing our energy efficiency. Forecasting energy use plays a fundamental role in building energy planning, management and optimization. The most common approaches for building energy forecasting include physics and data-driven models. Among the data-driven models, deep learning techniques have begun to emerge in recent years due to their: improved abilities in handling large amounts of data, feature extraction characteristics, and improved abilities in modelling nonlinear phenomena. This paper provides an extensive review of deep learning-based techniques applied to forecasting the energy use in buildings to explore its effectiveness and application potential. First, we present a summary of published literature reviews followed by an overview of deep learning-based definitions and techniques. Next, we present a breakdown of current trends identified in published research along with a discussion of how deep learning-based models have been applied for feature extraction and forecasting. Finally, the review concludes with current challenges faced and some potential future research directions.

Opportunities for Machine Learning in District Heating

“…ML offers several opportunities for problems related to monitoring of operations and optimisation. However, it has become increasingly clear that advances in DH-related ML research have a disproportionate focus on some problem areas, specifically forecasting [15][16][17][18], while missing out on several interesting problems, e.g., substation heat transfer, occupancy comfort, etc.…”

“…To understand existing literature reviews in this area, we provide an overview of the knowledge gaps identified in various studies, the types of ML models reported, datasets employed and building characteristics (features) studied. Most research articles on the application of ML in DH have occurred in the last 10 years [7,15,18]. Each of the review studies addressed different research gaps, considered research articles over different time periods and considered various building types.…”

“…Knowledge gaps addressed by previous review studies include: (1) lack of an overview of building performance models, in general [7,26], and the lack of data driven approaches for building performance, in particular [15,25], (2) lack of comprehensive studies of ML in urban building energy performance [7], (3) lack of ML performance and accuracy assessment on DH problems [16] and ( 4) lack of open datasets for benchmarking algorithms [15]. Other review studies highlight the need to compare the forecasting performance of common ML algorithms such as Artificial Neural Networks (ANN), Support Vector Machine (SVM) or Gaussian-Based Regression (GBR) [7,18]. Another gap is the lack of discussion of the key strengths and weaknesses of "white-box" models, "black-box" models, but also hybrid models, with focus on building modelling and energy performance prediction [26].…”

“…A similar review [7] came up with the conclusion that ANN, Support Vector Regression (SVR), Genetic Algorithm (GA) and Random Forest (RF) were common methods applied to individual buildings. Reviews have also focused on specific methods, such as deep learning [18] and ANN [17].…”

Intelligent Buildings in Smart Grids: A Survey on Security and Privacy Issues Related to Energy Management

“…Deep learning techniques are a solution that is becoming more and more popular in recent years for BEMS. One interesting application is the forecasting of the energy consumption in buildings in order to implement adapted mechanisms to optimize the energy management [95]. The BEMS based on MAS have also combined some DL techniques, for making agents more adaptative and intelligent.…”