Journal
ENERGY
Volume 263, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2022.126082
Keywords
Solar energy; Photovoltaics; Building -integrated photovoltaics; Smart energy systems; Data -driven approach
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This paper summarizes the sociological and engineering challenges of smart building-integrated photovoltaic (SBIPV) systems and proposes a data-driven solution. Data Sensing, Data Analysis, Data-driven Prediction, and Data-driven Optimization are the key steps to achieve a data-driven SBIPV system. Additionally, the technologies and models for data-driven SBIPV systems are explored to enable automated operational decisions.
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.
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