Open AccessReview of Different Error Metrics: A Case of Solar Forecasting
Author(s) -
Pardeep Singla,
Manoj Duhan,
Sumit Saroha
Publication year - 2021
Publication title -
the aiub journal of science and engineering
Language(s) - English
Resource type - Journals
eISSN - 2520-4890
pISSN - 1608-3679
DOI - 10.53799/ajse.v20i4.212
Subject(s) - probabilistic forecasting , computer science , probabilistic logic , renewable energy , solar irradiance , set (abstract data type) , grid , solar energy , solar power , data mining , reliability engineering , industrial engineering , power (physics) , artificial intelligence , meteorology , engineering , mathematics , physics , geometry , quantum mechanics , electrical engineering , programming language
Renewable energy systems (RES) are no longer confined to being used as a stand-alone entity in the modern era. These RES, especially solar panels are also used with the grid power systems to supply electricity. However, precise forecasting of solar irradiance is necessary to ensure that the grid operates in a balanced and planned manner. Various solar forecasting models (SFM) are presented in the literature to produce an accurate solar forecast. Nevertheless, each model has gone through the step of evaluation of its accuracy using some error measures. Many error measures are discussed in the literature for deterministic as well as probabilistic solar forecasting. But, each study has its own selected error measure which sometimes landed on a wrong interpretation of results if not selected appropriately. As a result, this paper offers a critical assessment of several common error metrics with the goal of discussing alternative error metrics and establishing a viable set of error metrics for deterministic and probabilistic solar forecasting. Based on highly cited research from the last three years (2019-2021), error measures for both types of forecasting are presented with their basic functionalities, advantages & limitations which equipped the reader to pick the required compatible metrics