Improving Wind Power Generation Forecasts: A Hybrid ANN-Clustering-PSO Approach

Abstract

This study introduces a novel hybrid forecasting model for wind power generation. It integrates Artificial Neural Networks, data clustering, and Particle Swarm Optimization algorithms. The methodology employs a systematic framework: initial clustering of weather data via the k-means algorithm, followed by Pearson’s analysis to pinpoint pivotal elements in each cluster. Subsequently, a Multi-Layer Perceptron Artificial Neural Network undergoes training with a Particle Swarm Optimization algorithm, enhancing convergence and minimizing prediction discrepancies. An important focus of this study is to streamline wind forecasting. By judiciously utilizing only sixteen observation points near a wind farm plant, in contrast to the complex global numerical weather prediction systems employed by the European Center Medium Weather Forecast, which rely on thousands of data points, this approach not only enhances forecast accuracy but also significantly simplifies the modeling process. Validation is performed using data from the Italian National Meteorological Centre. Comparative assessments against both a persistence model and actual wind farm data from Southern Italy substantiate the superior performance of the proposed hybrid model. Specifically, the clustered Particle Swarm Optimization-Artificial Neural Network-Wind Forecasting Method demonstrates a noteworthy improvement, with a reduction in mean absolute percentage error of up to 59.47% and a decrease in root mean square error of up to 52.27% when compared to the persistence model.

Load Margin Assessment of Power Systems Using Physics-Informed Neural Network with Optimized Parameters

“…It is a bio-inspired algorithm with few operations, simple to implement and effective in obtaining satisfactory solutions to optimization problems. There are several applications of PSO in the literature [99][100][101][102][103]. The positions of the particles are the variables of the search problem and are updated according to the velocity values of each particle, which can be mathematically formulated as…”

Load Day-Ahead Automatic Generation Control Reserve Capacity Demand Prediction Based on the Attention-BiLSTM Network Model Optimized by Improved Whale Algorithm

“…Unplanned fluctuations in renewable energy sources and loads pose challenges to the security and stability of the power system and the balance between energy supply and demand [3,4]. Inertia is an integral and important part of future power systems [5], yet the new energy itself has a small moment of inertia [6][7][8][9], which is also unfavorable for the power system. New energy output is characterized by high volatility and randomness; while the penetration of new energy sources in the power system is increasing, the power system needs to prepare more reserve capacity to cope with the volatility of their output [10,11].…”