| Abstract |
Under the background of smart city, the concepts of 'green building' and 'net-zero energy building' become more and more popular for reducing the building power consumption. As a result, the technologies related to the design and intelligent control of building integrated green energy system develop rapidly in recent years. In this study, the topological structure of large-scale building integrated photovoltaic (BIPV) system is analyzed, and a novel data-driven maximum power point tracking (MPPT) methodology is developed. To be specific, several characteristic-variables for achieving efficient MPPT of large-scale BIPV system are proposed, and the data-driven MPPT model based on deep neural network (DNN) is developed. Then, the developed characteristic-variables and DNN model are verified by a comprehensive set of numerical experiments. The optimal DNN structure is also verified in detail in this study. In addition, in order to dynamically track the degradation of photovoltaic module and overcome its influence on DNN model, the time-window mechanism of BIPV knowledge-base is introduced, and the optimal length of time-window for different DNN structures is verified by numerical experiments. Experimental results show that the DNN model with developed characteristic-variables and time-window mechanism achieves accurate and robust forecasting performance on the MPPT of large-scale BIPV system. © 2022 - IOS Press. All rights reserved. |
