Generative Adversarial Network-Assisted Framework for Power Management.

The rise in power consumption (PC) is caused by several factors such as the growing global population, urbanization, technological advances, economic development, and growth of businesses and commercial sectors. In these days, intermittent renewable energy sources (RESs) are widely utilized in electric grids to meet the need for power. Data-driven techniques are essential to assuring the steady operation of the electric grid and accurate power consumption and generation forecasting. Conversely, the available datasets for time series electric power forecasting in the energy industry are not as large as those for other domains such as in computer vision. Thus, a deep learning (DL) framework for predicting PC in residential and commercial buildings as well as the power generation (PG) from RESs is introduced. The raw power data obtained from buildings and RESs-based power plants are conceded by the purging process where absent values are filled in and noise and outliers are eliminated. Next, the proposed generative adversarial network (GAN) uses a portion of the cleaned data to generate synthetic parallel data, which is combined with the actual data to make a hybrid dataset. Subsequently, the stacked gated recurrent unit (GRU) model, which is optimized for power forecasting, is trained using the hybrid dataset. Six existent power data are used to train and test sixteen linear and nonlinear models for energy forecasting. The best-performing network is selected as the proposed method for forecasting tasks. For Korea Yeongam solar power (KYSP), individual household electric power consumption (IHEPC), and advanced institute of convergence technology (AICT) datasets, the proposed model obtains mean absolute error (MAE) values of 0.0716, 0.0819, and 0.0877, respectively. Similarly, its MAE values are 0.1215, 0.5093, and 0.5751, for Australia Alice Springs solar power (AASSP), Korea south east wind power (KSEWP), and, Korea south east solar power (KSESP) datasets, respectively. [ABSTRACT FROM AUTHOR]