A Review of Methodologies for Photovoltaic Energy Generation Forecasting in the Building Sector

Omid Pedram, Ana Soares and Pedro Moura ()
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Omid Pedram: Department of Electrical and Computer Engineering, University of Coimbra, 3030-290 Coimbra, Portugal
Ana Soares: Department of Electrical and Computer Engineering, University of Coimbra, 3030-290 Coimbra, Portugal
Pedro Moura: Department of Electrical and Computer Engineering, University of Coimbra, 3030-290 Coimbra, Portugal

Energies, 2025, vol. 18, issue 18, 1-52

Abstract: Photovoltaic (PV) systems are swiftly expanding within the building sector, offering significant benefits such as renewable energy integration, yet introducing challenges due to mismatches between local generation and demand. With the increasing availability of data and advanced modeling tools, stakeholders are increasingly motivated to adopt energy management and optimization techniques, where accurate forecasting of PV generation is essential. While the existing literature provides valuable insights, a comprehensive review of methodologies specifically tailored for the forecast of PV generation in buildings remains scarce. This study aims to address this gap by analyzing the forecasting methods, data requirements, and performance metrics employed, with the primary objective of providing an in-depth review of previous research. The findings highlight the critical role of improving PV energy generation forecasting accuracy in enhancing energy management and optimization for individual buildings. Additionally, the study identifies key challenges and opportunities for future research, such as the limited exploration of localized environmental and operational factors (such as partial shading, dust, and dirt); insufficient data on building-specific PV output patterns; and the need to account for variability in PV generation. By clarifying the current state of PV energy forecasting methodologies, this research lays essential groundwork for future advancements in the field.

Keywords: building-attached photovoltaic; building-integrated photovoltaic; photovoltaic power generation forecasting; Artificial Intelligence; Artificial Neural Networks; Machine Learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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