Computationally intelligent energy forecasting methods for appropriate energy management at the consumer/producer side have a positive impact on the preservation of energy and play a constructive role in tackling global climate change. The energy production and consumption are very high worldwide, demanding intelligent methods with real-world implementation potentials for appropriate energy management. In this paper, we survey the existing intelligent load forecasting (ILF) systems, highlight their advantages and downsides, and briefly discuss the workflow of the employed literature. Furthermore, we debate on the existing load forecasting datasets and their features along with a brief overview of the challenges confronted by researchers using these datasets. Distinct from previous survey papers, we provide a detailed review of performance evaluation metrics and comparison of employed methods for energy load forecasting, thereby concluding the need of efficient, effective, and adoptable ILF methods functional in real-world scenarios. Finally, we assess the employed techniques and deliver future research opportunities based on the derived conclusions from existing research works. This paper delivers the overall energy forecasting literature in a compact form with possible future insights for researchers working in ILF domain.
Bibliographical noteFunding Information:
National Research Foundation of Korea Funding information
© 2021 The Authors. International Journal of Energy Research published by John Wiley & Sons Ltd.
- energy consumption modeling
- energy management
- energy monitoring
- energy survey
- intelligent load forecasting
- smart energy systems
ASJC Scopus subject areas
- Renewable Energy, Sustainability and the Environment
- Nuclear Energy and Engineering
- Fuel Technology
- Energy Engineering and Power Technology