A Prediction Model for Ultra-Short-Term Output Power of Wind Farms Based on Deep Learning

Abstract

The output power prediction of wind farm is the key to effective utilization of wind energy and reduction of wind curtailment. However, the prediction of output power has long been a difficulty faced by both academia and the wind power industry, due to the high stochasticity of wind energy. This paper attempts to improve the ultra-short-term prediction accuracy of output power in wind farm. For this purpose, an output power prediction model was constructed for wind farm based on the time sliding window (TSW) and long short-term memory (LSTM) network. Firstly, the wind power data from multiple sources were fused, and cleaned through operations like dimension reduction and standardization. Then, the cyclic features of the actual output powers were extracted, and used to construct the input dataset by the TSW algorithm. On this basis, the TSW-LSTM prediction model was established to predict the output power of wind farm in ultra-short-term. Next, two regression evaluation metrics were designed to evaluate the prediction accuracy. Finally, the proposed TSW-LSTM model was compared with four other models through experiments on the dataset from an actual wind farm. Our model achieved a super-high prediction accuracy 92.7% as measured by d_MAE, an evidence of its effectiveness. To sum up, this research simplifies the complex prediction features, unifies the evaluation metrics, and provides an accurate prediction model for output power of wind farm with strong generalization ability.

A comprehensive review on deep learning approaches in wind forecasting applications

“…Summarizing the researchers' work, we found that the wind energy hybrid prediction framework consists of four main steps: data pre-processing, predictor prediction, error post-processing, and model performance evaluation. The technologies that may be used in the entire forecasting process can be divided into 10 categories: denoising, outlier detection and correction, resampling, normalization, decomposition, feature engineering, residual error modelling, filter-based correction, predictor and optimization algorithm [7,11,[140][141][142]. There has been a detailed review of these technologies by scholars, and we will not repeat them here.…”

Ultra-Short-Term Wind Power Prediction Based on LSTM with Loss Shrinkage Adam

“…(BPNN) [12,13], support vector regression (SVR) [14][15][16][17] and the deep learning network [18,19]. Wind power has the characteristics of a time series and has strong non-linearity.…”

“…Statistical methods establish a model that can express the mapping relationship between input and output by mining the inherent laws between a large number of historical data [8]. Commonly used statistical methods include time series analysis [9][10][11], the BP neural network (BPNN) [12,13], support vector regression (SVR) [14][15][16][17] and the deep learning network [18,19]. Wind power has the characteristics of a time series and has strong non-linearity.…”

Prediction method of photovoltaic power based on combination of CEEMDAN-SSA-DBN and LSTM

“…Literature [6] proposed a photovoltaic power prediction method combining Support Vector Regression (SVR) and Kalman filter, but inaccurate parameter selection of SVR resulted in reduced prediction accuracy. Literature [7] combined the cuckoo search algorithm with SVR to enhance parameter selection yet failed to consider the missing data and abnormal cases. In literature [8], a BP neural network was optimized using the Sparrow Search Algorithm (SSA) to establish an SSA-BP PV power prediction model.…”