Review of Different Error Metrics: A Case of Solar Forecasting
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Abstract
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
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[1]
P. Singla, M. Duhan, and S. Saroha, “Review of Different Error Metrics: A Case of Solar Forecasting”, AJSE, vol. 20, no. 4, pp. 158 - 165, Dec. 2021.
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AJSE contents are under the terms of the Creative Commons Attribution License. This permits anyone to copy, distribute, transmit and adapt the worknon-commercially provided the original work and source is appropriately cited.
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[12] M. J. Mayer and G. Gróf, “Extensive comparison of physical models for photovoltaic power forecasting,” Appl. Energy, vol. 283, p. 116239, Feb. 2021, doi: 10.1016/j.apenergy.2020.116239.
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[14] M. Guermoui, K. Gairaa, J. Boland, and T. Arrif, “A Novel Hybrid Model for Solar Radiation Forecasting Using Support Vector Machine and Bee Colony Optimization Algorithm: Review and Case Study,” J. Sol. Energy Eng., vol. 143, no. 2, Apr. 2021, doi: 10.1115/1.4047852.
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[17] P. Kumari and D. Toshniwal, “Long short term memory–convolutional neural network based deep hybrid approach for solar irradiance forecasting,” Appl. Energy, vol. 295, p. 117061, Aug. 2021, doi: 10.1016/J.APENERGY.2021.117061.
[18] G. Narvaez, L. F. Giraldo, M. Bressan, and A. Pantoja, “Machine learning for site-adaptation and solar radiation forecasting,” Renew. Energy, vol. 167, pp. 333–342, Apr. 2021, doi: 10.1016/J.RENENE.2020.11.089.
[19] T. Peng, C. Zhang, J. Zhou, and M. S. Nazir, “An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting,” Energy, vol. 221, p. 119887, Apr. 2021, doi: 10.1016/j.energy.2021.119887.
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