An Advanced Hybrid Forecasting System for Wind Speed Point Forecasting and Interval Forecasting

Abstract

Ultra-short-term wind speed prediction can assist the operation and scheduling of wind turbines in the short term and further reduce the adverse effects of wind power integration. However, as wind is irregular, nonlinear, and nonstationary, to accurately predict wind speed is a difficult task. To this end, researchers have made many attempts; however, they often use only point forecasting or interval forecasting, resulting in imperfect prediction results. Therefore, in this paper, we developed a prediction system integrating an advanced data preprocessing strategy, a novel optimization model, and multiple prediction algorithms. This combined forecasting system can overcome the inherent disadvantages of the traditional forecasting methods and further improve the prediction performance. To test the effectiveness of the forecasting system, the 10-min and one-hour wind speed sequences from the Sotavento wind farm in Spain were applied for conducting comparison experiments. The results of both the interval forecasting and point forecasting indicated that, in terms of the forecasting capability and stability, the proposed system was better than the compared models. Therefore, because of the minimum prediction error and excellent generalization ability, we consider this forecasting system to be an effective tool to assist smart grid programming.

An Experimental Machine Learning Approach for Mid-Term Energy Demand Forecasting

“…This action can be done by establishing an internal non-linear relationship between the input/output signals automatically by NN and performing the prediction by it in the next steps. A very important aspect of NNs is the updating of the parameters of NNs during the working phase adaptively and automatically [15], [26], [27]. The manifold learning scheme is developed in [28] for MTLF, the computations are simplified, and the suggested approach is examined by the use of data sets of New England.…”