Short-Term Electricity-Load Forecasting by Deep Learning: A Comprehensive Survey

TL;DR

This survey reviews the application of deep learning techniques to short-term electricity load forecasting over the past decade, highlighting advancements, challenges, and future research directions.

Contribution

It provides a comprehensive overview of deep learning methods in STELF, covering data processing, modeling, optimization, and evaluation, and discusses future research challenges.

Findings

Deep learning models have significantly improved forecasting accuracy.

Various data preprocessing and feature extraction techniques enhance model performance.

Identified key challenges and future directions in deep-learning-based STELF.

Abstract

Short-Term Electricity-Load Forecasting (STELF) refers to the prediction of the immediate demand (in the next few hours to several days) for the power system. Various external factors, such as weather changes and the emergence of new electricity consumption scenarios, can impact electricity demand, causing load data to fluctuate and become non-linear, which increases the complexity and difficulty of STELF. In the past decade, deep learning has been applied to STELF, modeling and predicting electricity demand with high accuracy, and contributing significantly to the development of STELF. This paper provides a comprehensive survey on deep-learning-based STELF over the past ten years. It examines the entire forecasting process, including data pre-processing, feature extraction, deep-learning modeling and optimization, and results evaluation. This paper also identifies some research challenges and potential research directions to be further investigated in future work.