Comprehensive review of load forecasting with emphasis on intelligent computing approaches

In this paper, a comprehensive review is presented for mid-term load forecasting. The basic loads and effective factors are studied, and then several classifications are presented for forecasting approaches. The main advantages and drawbacks of the approaches are analyzed. The neuro-fuzzy-based approaches are investigated in more detail, and their limitations are studied. Finally, some aspects are presented in the use of neuro-fuzzy systems for load forecasting. The main contributions are that: (1) A comprehensive review is presented such that both classical methods and new neuro-fuzzy approaches are investigated. (2) The basic methods are studied in details, and their achievements and drawbacks are discussed. (3) Some models and suggestions are presented for future practical applications. (4) Some categories are introduced for better evaluation of various methods. (C) 2022 The Author(s). Published by Elsevier Ltd.