Photovoltaic Reconfiguration and Residential Load Forecasting Using Deep Learning: A Literature Review
Keywords:
Photovoltaic arrays, partial shading, dynamic reconfiguration, deep learning, residential load forecasting, adaptive learning, energy managementAbstract
The integration of distributed photovoltaic generation into residential networks faces two persistent challenges, namely, power degradation under partial shading and demand variability driven by battery storage and electric vehicles. This work develops a bibliographic review with comparative quantitative analysis on photovoltaic array reconfiguration strategies and residential load forecasting models based on deep learning. Fifteen open-access sources published between 2020 and 2024 were examined, selected according to criteria of thematic relevance, availability of numerical data, and methodological rigor. The analysis was structured around four axes, that is, static and dynamic array configurations, irradiance and photovoltaic power forecasting, behind-the-meter residential load forecasting, and integration with energy storage systems. The findings, synthesized in two comparative tables, reveal power gains ranging between five and twenty-five percent through reconfiguration based on convolutional networks, as well as consistent reductions in forecasting error with hybrid LSTM-TCN and LSTM-SAE architectures. It is concluded that the convergence between reconfiguration and forecasting constitutes a promising avenue for residential energy management, although gaps remain in integrated experimental validation across diverse geographical contexts requiring further empirical research efforts oriented toward operational implementation in real residential settings.
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