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Content Provider | Directory of Open Access Journals (DOAJ) |
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Author | Liufeng Du Linghua Zhang Xiyan Tian |
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. |
e-ISSN | 21693536 |
DOI | 10.1109/ACCESS.2018.2869424 |
Journal | IEEE Access |
Volume Number | 6 |
Language | English |
Publisher | IEEE |
Publisher Date | 2018-01-01 |
Publisher Place | United States |
Access Restriction | Open |
Subject Keyword | Electrical Engineering. Electronics. Nuclear Engineering Building Attached Photovoltaic Power Forecasting Convolutional Neural Network Energy Internet Two-dimensional Fourier Transform |
Content Type | Text |
Resource Type | Article |
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