IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v334y2023ics0306261923000120.html
   My bibliography  Save this article

Machine learning for performance prediction in smart buildings: Photovoltaic self-consumption and life cycle cost optimization

Author

Listed:
  • Amini Toosi, Hashem
  • Del Pero, Claudio
  • Leonforte, Fabrizio
  • Lavagna, Monica
  • Aste, Niccolò

Abstract

The application of Photovoltaic (PV) system in buildings is growing rapidly in response to the need for clean energy sources and building decarbonization targets. Nonetheless, enhancing PV self-consumption through technical solutions such as Energy Storage Systems (ESS) is getting higher importance to increase the profitability of PV plants, by minimizing the building-grid interaction. In this context, analyzing PV self-consumption of different energy storage configurations becomes more relevant and crucial in building energy modeling although it is heavily time-consuming and complicated, particularly within a multi-objective optimization related to the ESS design. As a solution to resolve this issue, this paper evaluates the accuracy, training, and prediction speed of 24 Machine Learning (ML) models to be used as surrogate models for analyzing PV self-consumption in smart buildings. Furthermore, the performance of short-term Thermal Energy Storage (TES) to increase PV self-consumption is assessed and presented using ML models. The results showed the Gaussian Process Regression (GPR), Neural Networks (NN) including bilayered and trilayered NN models, Support Vector Machines (SVM) including the fine gaussian and cubic SVM models, and Ensembles of Trees (EoT) as superior ML models. The results also revealed that TES systems can efficiently increase PV self-consumption in the building equipped with electric heat pumps to provide heating, cooling, and domestic hot water. Moreover, the TES size optimization regarding the Life Cycle Cost (LCC) showed that the LCC-based optimum TES size can yield 7.1% savings within 30 years of the building service life. The novelties of this research are first to provide a reference to select the most suitable ML models in predicting PV self-consumption, second to implement machine learning for analyzing the performance of short-term thermal energy storage to enhance PV self-consumption in buildings, and third to carry out an LCC-based optimization on TES size using ML-based prediction models.

Suggested Citation

  • Amini Toosi, Hashem & Del Pero, Claudio & Leonforte, Fabrizio & Lavagna, Monica & Aste, Niccolò, 2023. "Machine learning for performance prediction in smart buildings: Photovoltaic self-consumption and life cycle cost optimization," Applied Energy, Elsevier, vol. 334(C).
    • Handle: RePEc:eee:appene:v:334:y:2023:i:c:s0306261923000120
    DOI: 10.1016/j.apenergy.2023.120648
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923000120
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.120648?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bui, Dac-Khuong & Nguyen, Tuan Ngoc & Ngo, Tuan Duc & Nguyen-Xuan, H., 2020. "An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings," Energy, Elsevier, vol. 190(C).
    2. Jonas Bielskus & Violeta Motuzienė & Tatjana Vilutienė & Audrius Indriulionis, 2020. "Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model," Energies, MDPI, vol. 13(15), pages 1-20, August.
    3. Azadeh Sadeghi & Roohollah Younes Sinaki & William A. Young & Gary R. Weckman, 2020. "An Intelligent Model to Predict Energy Performances of Residential Buildings Based on Deep Neural Networks," Energies, MDPI, vol. 13(3), pages 1-23, January.
    4. Zhou, Yi & Zhou, Nanrun & Gong, Lihua & Jiang, Minlin, 2020. "Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine," Energy, Elsevier, vol. 204(C).
    5. Gallego-Castillo, Cristobal & Heleno, Miguel & Victoria, Marta, 2021. "Self-consumption for energy communities in Spain: A regional analysis under the new legal framework," Energy Policy, Elsevier, vol. 150(C).
    6. Fathi, Soheil & Srinivasan, Ravi & Fenner, Andriel & Fathi, Sahand, 2020. "Machine learning applications in urban building energy performance forecasting: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    7. Shukhobodskiy, Alexander Alexandrovich & Colantuono, Giuseppe, 2020. "RED WoLF: Combining a battery and thermal energy reservoirs as a hybrid storage system," Applied Energy, Elsevier, vol. 274(C).
    8. Maljkovic, Danica & Basic, Bojana Dalbelo, 2020. "Determination of influential parameters for heat consumption in district heating systems using machine learning," Energy, Elsevier, vol. 201(C).
    9. Jack Ngarambe & Amina Irakoze & Geun Young Yun & Gon Kim, 2020. "Comparative Performance of Machine Learning Algorithms in the Prediction of Indoor Daylight Illuminances," Sustainability, MDPI, vol. 12(11), pages 1-22, June.
    10. Wang, Xiaoyang & Sun, Yunlin & Luo, Duo & Peng, Jinqing, 2022. "Comparative study of machine learning approaches for predicting short-term photovoltaic power output based on weather type classification," Energy, Elsevier, vol. 240(C).
    11. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    12. 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).
    13. Hwang, Jun Kwon & Yun, Geun Young & Lee, Sukho & Seo, Hyeongjoon & Santamouris, Mat, 2020. "Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system," Renewable Energy, Elsevier, vol. 149(C), pages 1227-1245.
    14. Amasyali, Kadir & El-Gohary, Nora, 2021. "Machine learning for occupant-behavior-sensitive cooling energy consumption prediction in office buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    15. Zekić-Sušac, Marijana & Mitrović, Saša & Has, Adela, 2021. "Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities," International Journal of Information Management, Elsevier, vol. 58(C).
    16. Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2020. "Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization," Applied Energy, Elsevier, vol. 271(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ren, Haoshan & Gao, Dian-ce & Ma, Zhenjun & Zhang, Sheng & Sun, Yongjun, 2024. "Data-driven surrogate optimization for deploying heterogeneous multi-energy storage to improve demand response performance at building cluster level," Applied Energy, Elsevier, vol. 356(C).
    2. Abdellah Benabdelkader & Azeddine Draou & Abdulrahman AlKassem & Toufik Toumi & Mouloud Denai & Othmane Abdelkhalek & Marwa Ben Slimene, 2023. "Enhanced Power Quality in Single-Phase Grid-Connected Photovoltaic Systems: An Experimental Study," Energies, MDPI, vol. 16(10), pages 1-23, May.
    3. Dongli Tan & Yao Wu & Zhiqing Zhang & Yue Jiao & Lingchao Zeng & Yujun Meng, 2023. "Assessing the Life Cycle Sustainability of Solar Energy Production Systems: A Toolkit Review in the Context of Ensuring Environmental Performance Improvements," Sustainability, MDPI, vol. 15(15), pages 1-37, July.
    4. Yi, Jun & Qi, ZhongLi & Li, XiangChengZhen & Liu, Hong & Zhou, Wei, 2024. "Spatial correlation-based machine learning framework for evaluating shale gas production potential: A case study in southern Sichuan Basin, China," Applied Energy, Elsevier, vol. 357(C).
    5. He, Zhiyue & Tang, Yong & He, Youwei & Qin, Jiazheng & Hu, Shilai & Yan, Bicheng & Tang, Liangrui & Sepehrnoori, Kamy & Rui, Zhenhua, 2024. "Wellbore salt-deposition risk prediction of underground gas storage combining numerical modeling and machine learning methodology," Energy, Elsevier, vol. 305(C).

    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. Zheng, Lingwei & Su, Ran & Sun, Xinyu & Guo, Siqi, 2023. "Historical PV-output characteristic extraction based weather-type classification strategy and its forecasting method for the day-ahead prediction of PV output," Energy, Elsevier, vol. 271(C).
    2. Khalid Almutairi & Salem Algarni & Talal Alqahtani & Hossein Moayedi & Amir Mosavi, 2022. "A TLBO-Tuned Neural Processor for Predicting Heating Load in Residential Buildings," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    3. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    4. Boza, Pal & Evgeniou, Theodoros, 2021. "Artificial intelligence to support the integration of variable renewable energy sources to the power system," Applied Energy, Elsevier, vol. 290(C).
    5. Leijiao Ge & Tianshuo Du & Changlu Li & Yuanliang Li & Jun Yan & Muhammad Umer Rafiq, 2022. "Virtual Collection for Distributed Photovoltaic Data: Challenges, Methodologies, and Applications," Energies, MDPI, vol. 15(23), pages 1-24, November.
    6. Yuan-Kang Wu & Cheng-Liang Huang & Quoc-Thang Phan & Yuan-Yao Li, 2022. "Completed Review of Various Solar Power Forecasting Techniques Considering Different Viewpoints," Energies, MDPI, vol. 15(9), pages 1-22, May.
    7. Kapp, Sean & Choi, Jun-Ki & Hong, Taehoon, 2023. "Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
    8. Jiang, Feifeng & Ma, Jun & Li, Zheng & Ding, Yuexiong, 2022. "Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model," Energy, Elsevier, vol. 249(C).
    9. Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    10. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
    11. Zhang, Xu & Sun, Yongjun & Gao, Dian-ce & Zou, Wenke & Fu, Jianping & Ma, Xiaowen, 2022. "Similarity-based grouping method for evaluation and optimization of dataset structure in machine-learning based short-term building cooling load prediction without measurable occupancy information," Applied Energy, Elsevier, vol. 327(C).
    12. 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.
    13. Zini, Marco & Carcasci, Carlo, 2023. "Machine learning-based monitoring method for the electricity consumption of a healthcare facility in Italy," Energy, Elsevier, vol. 262(PB).
    14. Dhowmya Bhatt & Danalakshmi D & A. Hariharasudan & Marcin Lis & Marlena Grabowska, 2021. "Forecasting of Energy Demands for Smart Home Applications," Energies, MDPI, vol. 14(4), pages 1-19, February.
    15. Gao, Zhikun & Yang, Siyuan & Yu, Junqi & Zhao, Anjun, 2024. "Hybrid forecasting model of building cooling load based on combined neural network," Energy, Elsevier, vol. 297(C).
    16. Sabadus, Andreea & Blaga, Robert & Hategan, Sergiu-Mihai & Calinoiu, Delia & Paulescu, Eugenia & Mares, Oana & Boata, Remus & Stefu, Nicoleta & Paulescu, Marius & Badescu, Viorel, 2024. "A cross-sectional survey of deterministic PV power forecasting: Progress and limitations in current approaches," Renewable Energy, Elsevier, vol. 226(C).
    17. Gao, Tian & Niu, Dongxiao & Ji, Zhengsen & Sun, Lijie, 2022. "Mid-term electricity demand forecasting using improved variational mode decomposition and extreme learning machine optimized by sparrow search algorithm," Energy, Elsevier, vol. 261(PB).
    18. Liu, Xiaoqi & Lee, Seungjae & Bilionis, Ilias & Karava, Panagiota & Joe, Jaewan & Sadeghi, Seyed Amir, 2021. "A user-interactive system for smart thermal environment control in office buildings," Applied Energy, Elsevier, vol. 298(C).
    19. Yong-gang Zhang & Junbo Qiu & Yan Zhang & Yongyao Wei, 2021. "The adoption of ELM to the prediction of soil liquefaction based on CPT," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(1), pages 539-549, May.
    20. Amal A. Al-Shargabi & Abdulbasit Almhafdy & Dina M. Ibrahim & Manal Alghieth & Francisco Chiclana, 2021. "Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics," Sustainability, MDPI, vol. 13(22), pages 1-20, November.

    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:eee:appene:v:334:y:2023:i:c:s0306261923000120. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.