Load and Renewable Energy Forecasting Using Deep Learning for Grid Stability
This addresses grid stability challenges for operators integrating unpredictable renewable energy sources, but appears incremental as it builds on existing deep learning techniques.
The paper tackles the problem of short-term load and renewable energy forecasting to stabilize the grid by balancing supply and demand, using deep learning methods like CNN and LSTM, but does not report specific numerical results.
As the energy landscape changes quickly, grid operators face several challenges, especially when integrating renewable energy sources with the grid. The most important challenge is to balance supply and demand because the solar and wind energy are highly unpredictable. When dealing with such uncertainty, trustworthy short-term load and renewable energy forecasting can help stabilize the grid, maximize energy storage, and guarantee the effective use of renewable resources. Physical models and statistical techniques were the previous approaches employed for this kind of forecasting tasks. In forecasting renewable energy, machine learning and deep learning techniques have recently demonstrated encouraging results. More specifically, the deep learning techniques like CNN and LSTM and the conventional machine learning techniques like regression that are mostly utilized for load and renewable energy forecasting tasks. In this article, we will focus mainly on CNN and LSTM-based forecasting methods.