Wind Power Short-Term Forecasting Method Based on LSTM and Multiple Error Correction

Abstract

To improve the accuracy of short-term wind power prediction, a short-term wind power prediction model based on the LSTM model and multiple error correction is proposed. First, an affine wind power correction model based on assimilative migration is established to reduce the errors caused by false positives from the initial data. Then, a self-moving window LSTM prediction model based on the improved particle swarm optimization algorithm was established. By improving the particle swarm optimization algorithm, the optimal hidden neuron number and the optimal learning rate of the LSTM model were calculated to enhance the model’s accuracy. Definitively, the idea of error feedback prediction is used to correct the initial prediction error, and the prediction error is fed back to the LSTM model to reduce the error caused by the calculation of the LSTM model. By starting from the initial data error, model accuracy error, and model prediction error, multiple error correction of wind power is realized to improve the model accuracy. The simulation results show that the method improves the model’s prediction accuracy by using assimilative transfer and error feedback, contributing to the economic operation and sustainable development of the power system. Unlike traditional improvement ideas, the proposed improvement ideas do not involve the inherent characteristics of the original prediction methods. This method does not need to introduce other auxiliary methods and has good universality.

Enhancing Predictions of Acetate and Ethanol Production from Microbial Electrosynthesis Using Optimized Machine Learning Models

“…Utilizing inputs such as time, pH, temperature, applied potential, IC concentration, and current, we trained the RF model to predict the acetate concentration. During model training, prediction error plots and residual plots are usually utilized to verify the model accuracy. , The prediction error plot is considered an alternative measure for ML performance, while residual plot helps detect anomalies hidden in modeling attribute tables or activity tables that are difficult to identify using statistical parameters . Therefore, we used these methods to analyze the model training accuracy.…”

Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023

“…Chen et al [104] applied a multi-objective error regression method for error correction in prediction results, resulting in an improvement of over 26% in short-term WPP performance. Xiao et al [105] proposed a time-correlated, statistics-based error correction algorithm, further enhancing the prediction accuracy of the short-term wind power hybrid prediction model. After error correction, the RMSE and mean absolute error (MAE) were reduced by 9.14% and 14.96%, respectively.…”

“…In [123], to ensure the accuracy and stability of WPP, the key parameters of the DELM model underwent optimization through multi-objective crisscross optimization (MOCSO). In [105], an improvement to the PSO was employed to optimize the optimal number of hidden neurons and the optimal learning rate for the LSTM model, significantly enhancing the short-term accuracy of WPP. Suo et al [124] utilized the improved chimp optimization algorithm (IChOA) to optimize parameters for the BiGRU, proving its effectiveness in improving the predictive performance of BiGRU.…”

Short-Term Multi-Step Wind Direction Prediction Based on OVMD Quadratic Decomposition and LSTM

“…Compared to RNN, LSTM introduces gated self-circulation to solve these problems [33]. This innovation reduces dependence on information length [34] and realizes long-term tracking of time series data [35][36][37].…”