Modeling Energy Demand—A Systematic Literature Review

Abstract

In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.

Energy Demand Forecasting and Optimizing Electric Systems for Developing Countries

“…[7] 2021 Systematic ANN-based machine learning algorithms and focused on the specific problems tackled by those ANN-based models. [8] 2021 Systematic Energy demand modeling regarding: techniques, data, accuracy, energy carriers, sectors, and Spatio-temporal granularity. [9] 2020 Comprehensive Single and hybrid type predictive ML models based on short-term load forecasting.…”

“…Forecasting resolution is the time intervals considered for sequence-to-sequence prediction [8]. Single-step ahead forecasting predicts the following load values from historical load values.…”

A hybrid forecasting model using LSTM and Prophet for energy consumption with decomposition of time series data

“…Neural networks are also used widely for energy forecasting and these studies can be found in literature surveys ( Angelopoulos, Siskos & Psarras, 2019 ; Hong et al, 2020 ; Liu et al, 2020 ; Verwiebe et al, 2021 ; Wang et al, 2019 ). Artificial neural networks are used for quarterly energy demand forecasting of Australia, France, the USA ( Bannor & Acheampong, 2020 ), and Greece ( Ekonomou, 2010 ); grey neural networks are modeled for Spain ( Liu et al, 2020 ).…”

A Comparative Study of Statistical and Deep Learning Models for Energy Load Prediction

“…Researchers are provided with a systematic literature review of a considerable number of articles on energy demand modeling. Reference [8] reviewed and offered a classification of different techniques used in energy demand. There is also a lot of work done especially in machine learning (ML) techniques which A comparative study of statistical and deep learning models for energy load prediction rely on historic data and are extensively used to short-term forecasting [9].…”