A review of data-driven smart building-integrated photovoltaic systems: Challenges and objectives. (15th January 2023)

• Record Type: Journal Article
• Authors: Liu, Zhengguang; Guo, Zhiling; Chen, Qi; Song, Chenchen; Shang, Wenlong; Yuan, Meng; Zhang, Haoran
• Is Part Of: Energy. Volume 263:Part E(2023)
• Publication Date: 2023-01-15
• Subjects: Solar energy -- Photovoltaics -- Building-integrated photovoltaics -- Smart energy systems -- Data-driven approach; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.126082
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The smart building-integrated photovoltaic (SBIPV) systems have become the important source of electricity in recent years. However, many sociological and engineering challenges caused by temporal and spatial changes on demand-side and supply-side remain. In this paper, the barriers and traditional data utilization of SBIPV system causing the above challenges are summarized. Data-driven SBIPV was firstly proposed, including four aspects: Data Sensing, Data Analysis, Data-driven Prediction, and Data-driven Optimization. Data sensing goes beyond the technical limitations of a single measurement and can build the bridge between demand- and supply-side. Then, the demand-side response and electricity changes in supply-side under various environmental changes will also become clear by Data Analysis. Data-driven Prediction of load and electricity supply for the SBIPV is the basis of energy management. Data-driven Optimization is the combination of demand-side trading and disturbed system optimization in the field of engineering and sociology. Furthermore, the perspective of data-driven SBIPV, technologies and models, including all four data-driven features to make automated operational decisions on demand- and supply-side are also explored. The data -driven SBIPV system requiring much greater policy ambition and more effort from both supply and demand side, especially in the areas of data integration and the mitigation of SBIPV system. Highlights: The innovations of SBIPV were reviewed from the perspective of data. The barriers and challenges of traditional data utilization were summarized. Data-driven Sensing, Analysis, Prediction, and Optimization were illustrated. The perspectives of data-driven SBIPV systems were proposed.