Journal article

Publication year: 2019

Pages: 312-322

Journal: Renewable and Sustainable Energy Reviews

Volume number: 108

ISSN: 1364-0321

eISSN: 1879-0690

DOI Link: https://doi.org/10.1016/j.rser.2019.03.029

Publisher: Elsevier

Abstract: 

Propelled by the actual demand from the renewable energy industry, the progress of literature on quantitative forecasting models during the past years is extensive. Research provides a vast output of papers on wind speed, wind power, solar irradiance and solar power forecasting models, accompanied by models for energy load and price forecasting for short-term (e.g. for the intraday trading schemes available at many market places) to medium-term (e.g. for day-ahead trading) usage. While the models themselves are, mostly, rather sophisticated, the statistical evaluation of the results sometimes leaves headroom for improvement. Unfortunately, the latter may occasionally result in the rejection of papers.

This review aims at giving support at this point: It provides a guide on how to avoid typical mistakes of presenting and evaluating the results of forecasting models. The best practice of forecasting accuracy evaluation, benchmarking, and graphically/tabularly presenting forecasting results is shown. We discuss techniques, examples, guide to a set of paragon papers, and clarify on a state-of-the-art minimum standard of proceeding with the submission of renewable energy forecasting research papers.

Harvard Citation style: Croonenbroeck, C. and Stadtmann, G. (2019) Renewable generation forecast studies - Review and good practice guidance, Renewable and Sustainable Energy Reviews, 108, pp. 312-322. https://doi.org/10.1016/j.rser.2019.03.029

APA Citation style: Croonenbroeck, C., & Stadtmann, G. (2019). Renewable generation forecast studies - Review and good practice guidance. Renewable and Sustainable Energy Reviews. 108, 312-322. https://doi.org/10.1016/j.rser.2019.03.029