Abstract
Renewable energy plays an important role in the power mix of India being sustainable and environmental source of energy. In this study, modified fuzzy Q-learning (MFQL)-based solar radiation forecasting has been proposed to forecast 30-min-ahead solar irradiance. Application of MFQL is novel in this field, as it uses reinforcement learning and model-free environment. Raw data have been collected for four Indian cities in the state of Rajasthan, i.e. Jodhpur, Ajmer, Jaipur and Kota via the data portal of National Institute of Wind Energy and Wind Resource (NIWE). Empirical mode decomposition (EMD) has been used as the data pre-processing technique, and relevant features are extracted from Pearson’s correlation coefficient. The results obtained from the MFQL forecaster are promising with forecasting accuracy of 92.38% for winter, 93.73% for summer, 91.54% for monsoon and 92.05% for autumn season for the city of Ajmer, and similar results have been obtained for other cities as well. MFQL lends itself as an effective tool for forecasting of seasonal solar irradiance. Proposed prediction model can be effectively utilized for solar irradiance forecasting and for optimal generation of power from incident solar radiation.
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The GHI or global horizontal irradiance (in W/m2) is collected from NIWE national data portal. (https://niwe.res.in/).
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TS contributed to writing—original draft, data curation, methodology, conceptualization, validation, visualization and software; RS contributed to writing review and editing, supervision, formal analysis and investigation. JKK contributed to writing—review and editing, data curation, formal analysis, software and validation.
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Shikhola, T., Sharma, R. & Kohli, J.K. Seasonal prediction of solar irradiance with modified fuzzy Q-learning. Soft Comput 28, 4435–4455 (2024). https://doi.org/10.1007/s00500-023-08817-2
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DOI: https://doi.org/10.1007/s00500-023-08817-2