Deep Power Forecasting Model for Building Attached Photovoltaic System

Abstract

Geographical dispersion and output power fluctuations are the major barriers to efficient utilization and grid connection of building attached photovoltaic (BAPV). To eliminate these negative factors, a reliable energy management system and an accurate power forecasting model are necessary. In this paper, we first design an energy management micro-grid based on the energy Internet, which aims to tackle the problems faced by the grid-connected BAPV through the effective dual-flow management of energy and information. In the context of the proposed micro-grid, we propose a deep power forecasting model that employs a convolutional neural network to find the nonlinear relationship between meteorological information and BAPV power, while the data fed to the model are obtained through the 2-D Fourier transform of meteorological data. We evaluate the proposed model based on real-world meteorological and power data sets. Numerical results highlight the superiority of our forecasting model in terms of accuracy and reliability.

Keywords

• Building attached photovoltaic
• power forecasting
• convolutional neural network
• energy Internet
• two-dimensional Fourier transform