1. Introduction
With the continuous consumption of fossil fuels in the electric power industry, the environmental pollution caused by burning fossil fuels is becoming more and more serious [
1]. The proportion of thermal power generation in the world is gradually decreasing. Clean energy such as wind, solar and tidal energy has been developing quickly [
2]. Wind energy supply in most countries is abundant and inexhaustible, and wind energy conversion technology is relatively simple, so it is included in the key considerations of many countries. Wind energy’s extreme volatility, intermittency, and randomness may cause power fluctuations and have an impact on the regional grid’s overall operation [
3]. As a result, when wind farms, particularly high-capacity wind farms, are connected to the grid, they introduce some hidden dangers to the overall power system’s safety and stability [
4]. These variable, intermittent, and random characteristics will have a significant impact on wind turbine power’s generating efficiency and service life. As a possible solution to the aforementioned issues, a power dispatching management system requires an effective wind power forecast method in order to design a reasonable power generating plan and improve the grid’s economy, safety, and reliability [
5,
6,
7].
The problem of wind power prediction has attracted a lot of attention from researchers in related fields. Recently released wind power prediction methods can be divided into three categories: physical, statistical, and machine learning methods from the methodological point of view.
The physical method uses a numerical weather forecast produced by a meteorological service to mimic the weather in the wind farm area. The weather data, physical facts surrounding the wind turbine, and the height of the wind turbine’s hub center are then combined to construct a prediction model. Finally, the forecasted power is determined using the wind turbine’s power curve [
8]. However, physical methods are greatly limited in wind power prediction and have poor prediction accuracy due to the limitations of complex mathematical calculations and the difficulty of accurately modeling environmental factors.
Statistical methods are adopted to fit complex functional relationships between historical data, weather forecasting data, and forecasting results through one or more mathematical tools. They are basically used to find mathematical patterns in a large amount of data and to adjust forecasting based on the patterns found from this data. Statistical models mainly include autoregressive models, autoregressive sliding average models, and integrated sliding average autoregressive models. However, wind power series data are highly stochastic and intermittent, making their data very complex, and these statistical models cannot extract the corresponding nonlinear features well [
9], so there is still much room for improvement in statistical prediction methods.
With the fast growth of deep learning in recent years, machine learning has been extensively used to load prediction. Methods for machine learning primarily include genetic algorithms, artificial neural networks, fuzzy logic, support vector machines, etc. [
10]. Since neural networks may potentially infinitely approximate any linear or nonlinear connection, they are frequently employed to tackle classification and regression issues. However, artificial neural networks have intrinsic disadvantages: they are computationally demanding, susceptible to local optima, and sensitive to starting parameters, among others. To circumvent these disadvantages, researchers have developed interval prediction of wind power generation based on particle swarm optimization back propagation neural networks [
11]. With the fast development of deep learning methods in the disciplines of image and natural language processing, many researchers have attempted to use these techniques to address other issues. The literature [
12] employs a single LSTM for predicting the operational state of a transformer. It considerably enhances the accuracy of predictions. For wind power prediction, the literature [
13] blends long short-term memory (LSTM) and attention techniques. In addition, deep learning models were utilized in the literature [
14] for energy power forecasting. The literature [
15] used PSO-LSTM for the short-term prediction of non-time-series electricity price signals and optimized the LSTM network input weights using particle swarm techniques. However, the effects of the particle swarm’s own weights and learning rate on the global optimal solution were not considered. In addition, in recent years, some improved advanced algorithms have been proposed for load forecasting [
16,
17,
18,
19,
20], and we will be showing a comparison between available and proposed technology and highlighting the novelty and advantages of these in
Table 1.
Wind power generation is highly dependent on meteorological variables such as wind velocity. Traditional forecasting techniques, such as analyzing the impacts of variables on wind power generation using Pearson correlation coefficients, increase the model’s complexity. Inspired by the attention mechanism for letter alignment in the field of natural language translation [
21], we built an attention mechanism model that enables the model to learn multiple weights for input data. In this research, an LSTM model with a two-stage attention mechanism is developed for forecasting wind power generation using the time series of wind power generation and relevant meteorological variables. Two components comprise the model: an encoder and a decoder. Based on the LSTM structure, the attention mechanism weights are learned for the input weather data during the encoding stage. A similar daily attention approach based on time windows is presented for the decoding step by comparing the encoder’s output at each moment. Meanwhile, LSTM networks usually use the Adam optimizer for the parameter optimization of neural networks, but the hyperparameters of the network are set by humans, and the setting of hyperparameters has a large impact on the fitting results of the model. Therefore, we will introduce the MPSO algorithm to perform the hyperparameter search for LSTM networks. Finally, we input all the information into the MPSO_ATT_LSTM model proposed in this paper to predict wind power. This paper is structured as follows:
Section 2 introduces particle swarm optimization (PSO) and proposes modified particle swarm optimization (MPSO).
Section 3 introduces the LSTM model with a two-stage attention mechanism.
Section 4 introduces the fused MPSO_ATT_LSTM model. Simulation experiments are conducted in
Section 5, and the results are quantitatively analyzed. The conclusion and outlook are presented in
Section 6.