A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A review of data-driven smart building-integrated photovoltaic systems: Challenges and objectives
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.
A review of data-driven smart building-integrated photovoltaic systems: Challenges and objectives
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.
A review of data-driven smart building-integrated photovoltaic systems: Challenges and objectives
Liu, Zhengguang (author) / Guo, Zhiling (author) / Chen, Qi (author) / Song, Chenchen (author) / Shang, Wenlong (author) / Yuan, Meng (author) / Zhang, Haoran (author)
2023-01-15
Liu , Z , Guo , Z , Chen , Q , Song , C , Shang , W , Yuan , M & Zhang , H 2023 , ' A review of data-driven smart building-integrated photovoltaic systems: Challenges and objectives ' , Energy , vol. 263 , 126082 . https://doi.org/10.1016/j.energy.2022.126082
Article (Journal)
Electronic Resource
English
DDC:
690
Smart solar concentrators for building integrated photovoltaic façades
| British Library Online Contents | 2016
Quantification of Wind-Driven Rain Intrusion in Building-Integrated Photovoltaic Systems
| BASE | 2021
| DOAJ | 2021