Recent advances in electricity price forecasting: A review of probabilistic forecasting

• Published in: Renewable & sustainable energy reviews Vol. 81; pp. 1548 - 1568
• Main Authors: Nowotarski, Jakub, Weron, Rafał
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2018
• Subjects: Autoregression; Day-ahead market; Electricity price forecasting; Neural network; Probabilistic forecast; Reliability; Sharpness; Autoregression; Day-ahead market; Probabilistic forecast; Electricity price forecasting; Neural network; Reliability; Sharpness
• ISSN: 1364-0321; 1879-0690
• DOI: 10.1016/j.rser.2017.05.234

Since the inception of competitive power markets two decades ago, electricity price forecasting (EPF) has gradually become a fundamental process for energy companies’ decision making mechanisms. Over the years, the bulk of research has concerned point predictions. However, the recent introduction of smart grids and renewable integration requirements has had the effect of increasing the uncertainty of future supply, demand and prices. Academics and practitioners alike have come to understand that probabilistic electricity price (and load) forecasting is now more important for energy systems planning and operations than ever before. With this paper we offer a tutorial review of probabilistic EPF and present much needed guidelines for the rigorous use of methods, measures and tests, in line with the paradigm of ‘maximizing sharpness subject to reliability’. The paper can be treated as an update and a further extension of the otherwise comprehensive EPF review of Weron [1] or as a standalone treatment of a fascinating and underdeveloped topic, that has a much broader reach than EPF itself.