Machine Learning Techniques in the Energy Consumption of Buildings: A Systematic Literature Review Using Text Mining and Bibliometric Analysis

Ahmed Abdelaziz,Vitor Santos,Miguel Sales Dias

doi:10.3390/en14227810

Abstract

The high level of energy consumption of buildings is significantly influencing occupant behavior changes towards improved energy efficiency. This paper introduces a systematic literature review with two objectives: to understand the more relevant factors affecting energy consumption of buildings and to find the best intelligent computing (IC) methods capable of classifying and predicting energy consumption of different types of buildings. Adopting the PRISMA method, the paper analyzed 822 manuscripts from 2013 to 2020 and focused on 106, based on title and abstract screening and on manuscripts with experiments. A text mining process and a bibliometric map tool (VOS viewer) were adopted to find the most used terms and their relationships, in the energy and IC domains. Our approach shows that the terms “consumption,” “residential,” and “electricity” are the more relevant terms in the energy domain, in terms of the ratio of important terms (TITs), whereas “cluster” is the more commonly used term in the IC domain. The paper also shows that there are strong relations between “Residential Energy Consumption” and “Electricity Consumption,” “Heating” and “Climate. Finally, we checked and analyzed 41 manuscripts in detail, summarized their major contributions, and identified several research gaps that provide hints for further research.

Highlights

Published: 22 November 2021Over the past 20 years, the world’s growing energy demand has led to a growing interest in energy efficiency in the residential, services, and public building sectors
This paper provides a systematic literature review on the topic of intelligent computing methods applied to the energy consumption of buildings (ECB)
Since 106 manuscripts is a large number for a manual analysis, we describe a text mining approach, aiming at discovering relevant terms in both intelligent computing models and energy consumption fields, that we performed in a stage prior to the full-text review of the retained 41 papers of this survey

Summary

Introduction

Over the past 20 years, the world’s growing energy demand has led to a growing interest in energy efficiency in the residential, services, and public building sectors. The energy consumption of buildings (ECB) is a big challenge in most European countries since buildings consume a large amount of energy, especially sustainability buildings in energy sectors (e.g., public buildings) [1]. European countries, notably Portugal, strive to enhance the energy efficiency in buildings while maintaining sufficient levels of thermal comfort and energy consumption, aiming at sustaining their economic and social levels [3]. The energy sector seeks to control energy consumption in general by analyzing available data sources and by studying different dimensions taken from analyzing data sources, such as natural gas and electricity usage data, residential building characteristics and the energy performance of building data, cooling, and heating systems data, or climate and weather forecast data, just to name a few [4]. Public authorities seek to guide citizen behavior to more efficient uses of Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations

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