IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i18p5007-d1754091.html

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

Author

Listed:
  • 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
    Institute for Systems Engineering and Computers at Coimbra, DEEC, University of Coimbra, 3030-290 Coimbra, Portugal)

  • Pedro Moura

    (Department of Electrical and Computer Engineering, University of Coimbra, 3030-290 Coimbra, Portugal
    Institute of Systems and Robotics, DEEC, University of Coimbra, 3030-290 Coimbra, Portugal)

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.

Suggested Citation

  • Omid Pedram & Ana Soares & Pedro Moura, 2025. "A Review of Methodologies for Photovoltaic Energy Generation Forecasting in the Building Sector," Energies, MDPI, vol. 18(18), pages 1-51, September.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:18:p:5007-:d:1754091
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/18/5007/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/18/5007/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yang, Dazhi & Wang, Wenting & Gueymard, Christian A. & Hong, Tao & Kleissl, Jan & Huang, Jing & Perez, Marc J. & Perez, Richard & Bright, Jamie M. & Xia, Xiang’ao & van der Meer, Dennis & Peters, Ian , 2022. "A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: Towards carbon neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    2. Woo-Gyun Shin & Ju-Young Shin & Hye-Mi Hwang & Chi-Hong Park & Suk-Whan Ko, 2022. "Power Generation Prediction of Building-Integrated Photovoltaic System with Colored Modules Using Machine Learning," Energies, MDPI, vol. 15(7), pages 1-17, April.
    3. Zhu, Nanyang & Wang, Ying & Yuan, Kun & Yan, Jiahao & Li, Yaping & Zhang, Kaifeng, 2024. "GGNet: A novel graph structure for power forecasting in renewable power plants considering temporal lead-lag correlations," Applied Energy, Elsevier, vol. 364(C).
    4. Jesús Polo & Nuria Martín-Chivelet & Carlos Sanz-Saiz, 2022. "BIPV Modeling with Artificial Neural Networks: Towards a BIPV Digital Twin," Energies, MDPI, vol. 15(11), pages 1-11, June.
    5. Reynolds, Jonathan & Ahmad, Muhammad Waseem & Rezgui, Yacine & Hippolyte, Jean-Laurent, 2019. "Operational supply and demand optimisation of a multi-vector district energy system using artificial neural networks and a genetic algorithm," Applied Energy, Elsevier, vol. 235(C), pages 699-713.
    6. Rial A. Rajagukguk & Raden A. A. Ramadhan & Hyun-Jin Lee, 2020. "A Review on Deep Learning Models for Forecasting Time Series Data of Solar Irradiance and Photovoltaic Power," Energies, MDPI, vol. 13(24), pages 1-23, December.
    7. Rao K, D.V. Siva Krishna & Premalatha, M. & Naveen, C., 2018. "Analysis of different combinations of meteorological parameters in predicting the horizontal global solar radiation with ANN approach: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 248-258.
    8. Karol Bot & Samira Santos & Inoussa Laouali & Antonio Ruano & Maria da Graça Ruano, 2021. "Design of Ensemble Forecasting Models for Home Energy Management Systems," Energies, MDPI, vol. 14(22), pages 1-37, November.
    9. Wen, Lulu & Zhou, Kaile & Yang, Shanlin & Lu, Xinhui, 2019. "Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting," Energy, Elsevier, vol. 171(C), pages 1053-1065.
    10. Zakari, Abdulrasheed & Khan, Irfan & Tan, Duojiao & Alvarado, Rafael & Dagar, Vishal, 2022. "Energy efficiency and sustainable development goals (SDGs)," Energy, Elsevier, vol. 239(PE).
    11. Taeyoung Kim & Jinho Kim, 2021. "A Regional Day-Ahead Rooftop Photovoltaic Generation Forecasting Model Considering Unauthorized Photovoltaic Installation," Energies, MDPI, vol. 14(14), pages 1-22, July.
    12. Hu, Yuqing & Cheng, Xiaoyuan & Wang, Suhang & Chen, Jianli & Zhao, Tianxiang & Dai, Enyan, 2022. "Times series forecasting for urban building energy consumption based on graph convolutional network," Applied Energy, Elsevier, vol. 307(C).
    13. de Queiroz Corrêa, Luiza & Bagnis, Diego & Rabelo Melo Franco, Pedro & Ferreira da Costa Junior, Esly & Oliveira Souza da Costa, Andréa, 2024. "Evaluating energy generation of a building-integrated organic photovoltaic vertical façade: A case study of Latin America's pioneering installation," Renewable Energy, Elsevier, vol. 220(C).
    14. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
    15. D'Agostino, D. & Minelli, F. & D'Urso, M. & Minichiello, F., 2022. "Fixed and tracking PV systems for Net Zero Energy Buildings: Comparison between yearly and monthly energy balance," Renewable Energy, Elsevier, vol. 195(C), pages 809-824.
    16. Scott, Connor & Ahsan, Mominul & Albarbar, Alhussein, 2023. "Machine learning for forecasting a photovoltaic (PV) generation system," Energy, Elsevier, vol. 278(C).
    17. Trigo-González, Mauricio & Cortés-Carmona, Marcelo & Marzo, Aitor & Alonso-Montesinos, Joaquín & Martínez-Durbán, Mercedes & López, Gabriel & Portillo, Carlos & Batlles, Francisco Javier, 2023. "Photovoltaic power electricity generation nowcasting combining sky camera images and learning supervised algorithms in the Southern Spain," Renewable Energy, Elsevier, vol. 206(C), pages 251-262.
    18. Jinhwa Jeong & Dongkyu Lee & Young Tae Chae, 2023. "A Novel Approach for Day-Ahead Hourly Building-Integrated Photovoltaic Power Prediction by Using Feature Engineering and Simple Weather Forecasting Service," Energies, MDPI, vol. 16(22), pages 1-21, November.
    19. Victor Oliveira Santos & Felipe Pinto Marinho & Paulo Alexandre Costa Rocha & Jesse Van Griensven Thé & Bahram Gharabaghi, 2024. "Application of Quantum Neural Network for Solar Irradiance Forecasting: A Case Study Using the Folsom Dataset, California," Energies, MDPI, vol. 17(14), pages 1-26, July.
    20. Dongkyu Lee & Jinhwa Jeong & Sung Hoon Yoon & Young Tae Chae, 2019. "Improvement of Short-Term BIPV Power Predictions Using Feature Engineering and a Recurrent Neural Network," Energies, MDPI, vol. 12(17), pages 1-17, August.
    21. Panjapornpon, Chanin & Bardeeniz, Santi & Hussain, Mohamed Azlan, 2023. "Improving energy efficiency prediction under aberrant measurement using deep compensation networks: A case study of petrochemical process," Energy, Elsevier, vol. 263(PC).
    22. Khan, Waqas & Walker, Shalika & Zeiler, Wim, 2022. "Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach," Energy, Elsevier, vol. 240(C).
    23. Liu, Da & Sun, Kun, 2019. "Random forest solar power forecast based on classification optimization," Energy, Elsevier, vol. 187(C).
    24. Li, Qingxiang & Zhu, Li & Sun, Yong & Lu, Lin & Yang, Yang, 2020. "Performance prediction of Building Integrated Photovoltaics under no-shading, shading and masking conditions using a multi-physics model," Energy, Elsevier, vol. 213(C).
    25. Jesús Polo & Nuria Martín-Chivelet & Miguel Alonso-Abella & Carlos Sanz-Saiz & José Cuenca & Marina de la Cruz, 2023. "Exploring the PV Power Forecasting at Building Façades Using Gradient Boosting Methods," Energies, MDPI, vol. 16(3), pages 1-12, February.
    26. Giuseppe Ciaburro & Gino Iannace, 2021. "Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review," Data, MDPI, vol. 6(6), pages 1-30, May.
    27. Hu, Zehuan & Gao, Yuan & Ji, Siyu & Mae, Masayuki & Imaizumi, Taiji, 2024. "Improved multistep ahead photovoltaic power prediction model based on LSTM and self-attention with weather forecast data," Applied Energy, Elsevier, vol. 359(C).
    28. Gheouany, Saad & Ouadi, Hamid & El Bakali, Saida, 2024. "Optimal active and reactive energy management for a smart microgrid system under the Moroccan grid pricing code," Energy, Elsevier, vol. 306(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mahdiyeh Tabatabaei & Ernesto Antonini, 2025. "Machine Learning for Optimizing Urban Photovoltaics: A Review of Static and Dynamic Factors," Sustainability, MDPI, vol. 17(18), pages 1-34, September.
    2. Yang, Yanru & Liu, Yu & Zhang, Yihang & Shu, Shaolong & Zheng, Junsheng, 2025. "DEST-GNN: A double-explored spatio-temporal graph neural network for multi-site intra-hour PV power forecasting," Applied Energy, Elsevier, vol. 378(PA).
    3. Pei, Jingyin & Dong, Yunxuan & Guo, Pinghui & Wu, Thomas & Hu, Jianming, 2024. "A Hybrid Dual Stream ProbSparse Self-Attention Network for spatial–temporal photovoltaic power forecasting," Energy, Elsevier, vol. 305(C).
    4. Qiu, Lihong & Ma, Wentao & Feng, Xiaoyang & Dai, Jiahui & Dong, Yuzhuo & Duan, Jiandong & Chen, Badong, 2024. "A hybrid PV cluster power prediction model using BLS with GMCC and error correction via RVM considering an improved statistical upscaling technique," Applied Energy, Elsevier, vol. 359(C).
    5. Hasnat, Md Abul & Asadi, Somayeh & Alemazkoor, Negin, 2025. "A graph attention network framework for generalized-horizon multi-plant solar power generation forecasting using heterogeneous data," Renewable Energy, Elsevier, vol. 243(C).
    6. Jesús Polo & Nuria Martín-Chivelet & Miguel Alonso-Abella & Carlos Sanz-Saiz & José Cuenca & Marina de la Cruz, 2023. "Exploring the PV Power Forecasting at Building Façades Using Gradient Boosting Methods," Energies, MDPI, vol. 16(3), pages 1-12, February.
    7. Sergio Cantillo-Luna & Ricardo Moreno-Chuquen & David Celeita & George Anders, 2023. "Deep and Machine Learning Models to Forecast Photovoltaic Power Generation," Energies, MDPI, vol. 16(10), pages 1-24, May.
    8. Zhang, Zongbin & Huang, Xiaoqiao & Li, Chengli & Cheng, Feiyan & Tai, Yonghang, 2025. "CRAformer: A cross-residual attention transformer for solar irradiation multistep forecasting," Energy, Elsevier, vol. 320(C).
    9. Wang, Tao & Xu, Ye & Qin, Yu & Wang, Xu & Zheng, Feifan & Li, Wei, 2025. "Short-term PV forecasting of multiple scenarios based on multi-dimensional clustering and hybrid transformer-BiLSTM with ECPO," Energy, Elsevier, vol. 334(C).
    10. Weixiong Wu & Rui Gao & Peng Wu & Chen Yuan & Xiaoling Xia & Renfeng Liu & Yifei Wang, 2024. "Enhanced Ultra-Short-Term PV Forecasting Using Sky Imagers: Integrating MCR and Cloud Cover Estimation," Energies, MDPI, vol. 18(1), pages 1-17, December.
    11. Khan, Waqas & Somers, Ward & Walker, Shalika & de Bont, Kevin & Van der Velden, Joep & Zeiler, Wim, 2023. "Comparison of electric vehicle load forecasting across different spatial levels with incorporated uncertainty estimation," Energy, Elsevier, vol. 283(C).
    12. Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Mohamed Trabelsi & Shady S. Refaat & Fakhreddine S. Oueslati, 2021. "Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements," Energies, MDPI, vol. 14(13), pages 1-20, July.
    13. Yanan Xue & Jinliang Yin & Xinhao Hou, 2024. "Short-Term Wind Power Prediction Based on Multi-Feature Domain Learning," Energies, MDPI, vol. 17(13), pages 1-25, July.
    14. Cheng, Lilin & Zang, Haixiang & Wei, Zhinong & Zhang, Fengchun & Sun, Guoqiang, 2022. "Evaluation of opaque deep-learning solar power forecast models towards power-grid applications," Renewable Energy, Elsevier, vol. 198(C), pages 960-972.
    15. Wang, Keqi & Wang, Lijie & Meng, Qiang & Yang, Chao & Lin, Yangshu & Zhu, Junye & Zhao, Zhongyang & Zhou, Can & Zheng, Chenghang & Gao, Xiang, 2025. "Accurate photovoltaic power prediction via temperature correction with physics-informed neural networks," Energy, Elsevier, vol. 328(C).
    16. Xia, Chenyue & Xu, Yinliang & Tai, Nengling & Sun, Hongbin, 2026. "A dual-layer feature-selection transformer network for transferable probabilistic forecasting of PV power," Applied Energy, Elsevier, vol. 406(C).
    17. Barancsuk, Lilla & Groma, Veronika & Kocziha, Barnabás, 2025. "Hybrid ultra-short term solar irradiation forecasting using resource-efficient multi-step long-short term memory," Renewable Energy, Elsevier, vol. 247(C).
    18. Wan, Hang & Wang, Jiasong & Gan, Quan & Xia, Yaping & Chang, Yufang & Yan, Huaicheng, 2025. "Addressing intermittency in medium-term photovoltaic and wind power forecasting using a hybrid xLSTM-TCCNN model with numerical weather predictions," Renewable Energy, Elsevier, vol. 253(C).
    19. Hu, Zehuan & Gao, Yuan & Sun, Luning & Mae, Masayuki & Imaizumi, Taiji, 2024. "Self-learning dynamic graph neural network with self-attention based on historical data and future data for multi-task multivariate residential air conditioning forecasting," Applied Energy, Elsevier, vol. 364(C).
    20. Udenze, Peter I. & Gong, Jiaqi & Soltani, Shohreh & Li, Dawen, 2025. "A deep neural network with two-step decomposition technique for predicting ultra-short-term solar power and electrical load," Applied Energy, Elsevier, vol. 382(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:18:p:5007-:d:1754091. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.