Adaptive cloudiness index for enhanced photovoltaic energy prediction and management in low-income smart homes using geographic information system

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Abstract

Solar-powered homes can be an optimal solution for the lack of continuous power sources problem in initial low-income communities. However, the challenge of Photovoltaic (PV) uncertainty can make it difficult to coordinate this vital solar energy in real-time. This paper proposes a new, low-cost solution for assessing the uncertainty of photovoltaic power generation in smart home energy management systems. The proposed index, inspired by the well-known clearness index, is an adaptive deterministic indicator that only requires free Geographic Information System (GIS) models and PV power measurement, without the need for expensive high-tech controllers or expert engineers/programmers. The proposed index successfully predicts the daily PV energy with errors of less than 3% for more than 93% of studied days, according to the 2020 measured solar radiation of the studied case in an African developing location, i.e. Cairo. Egypt.

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Elazab, R., Saif, O., Metwally, A. M. A. A., & Daowd, M. (2024). Adaptive cloudiness index for enhanced photovoltaic energy prediction and management in low-income smart homes using geographic information system. Discover Applied Sciences, 6(3). https://doi.org/10.1007/s42452-024-05793-6

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