1. Introduction
Electricity loads are basic and important information for power generation facilities and traders [
1], especially in terms of production plans, daily operations, unit commitments, and economic dispatches [
2]. Currently, based on the length of the forecasting time window, three types of load forecasting models are available [
3]. First, long-term load forecasting can be used to predict the power load situations of factories and infrastructure for a window of several years and help investors make decisions [
4]. Second, medium-term load forecasting can generally be used to predict the power load situation of a target area for time periods ranging from a few days to a few months [
5]. Third, short-term load forecasting (STLF), which can generally forecast power loads for only a few days or a few hours, is generally used for real-time power generation control, safety analysis, and energy transaction planning [
6]. STLF can be performed at the national, regional, or microgrid level [
7]. The supply–demand balance rule applies to the electricity market, with electricity prices increasing during periods with high electricity loads and decreasing during periods with low electricity loads (such as nights, weekends, and holidays) [
8]. It is worth noting that the power load is determined on an hourly basis. In a large power plant, a production plan is mostly carried out every day. In summary, STLF plays a vital role in managing the operations of the electricity market. In addition, due to the COVID-19 pandemic, the prices of the raw materials for use in electricity production have risen sharply [
9], which has made the supplies of electricity in many countries increasingly tight [
10]. This makes STLF more important [
11].
In STLF, selecting a suitable training set and building an optimized prediction model are the keys to improving the resulting prediction accuracy. Therefore, researchers have proposed many models in the past few decades. The existing STLF models are mainly based on artificial intelligence (AI) methods [
12]. At present, the main AI models for STLF include deep learning [
13], support vector machines (SVMs) [
14], and genetic models [
15]. For example, Barman et al. proposed a hybrid FA–SVM model for short-term load forecasting [
16].Liu et al. proposed a KF–BA–SVM model to predict the data of a substation in South China [
17].The latest research has shown that multilayer perceptron (MLP) [
18] and long short-term memory (LSTM) models [
19] perform STLF better than existing models [
20]. For example, Kong et al. proposed an LSTM model that can be used for household electric load forecasting [
21]. Mujeeb et al. proposed a power load forecasting method based on LSTM [
22]. An article by Kontogiannis et al. showed that the power load model based on an MLP is better than a convolutional neural network (CNN) model and an LSTM model [
23].
However, two main challenges are still faced by existing STLF models. The first involves how to fuse multiscale load data to achieve a higher performance than that of existing models and solve the data noise problem after integration. Compared with a single-scale training model, according to existing research, a multiscale STLF model can achieve a higher prediction performance [
24]. However, multiscale data increase the noise in the given data. This increases the difficulty of building an STLF model. The second challenge is how to prevent the STLF model from falling into local optimal solutions. Sliding window segmentation causes sample quality problems. The use of all samples for training makes it easy for the model to fall into a local optimum.
This paper proposes a multiscale fusion-based STLF model built on a sparse deep autoencoder and self-paced learning (SPL). The sparse deep autoencoder proposed in this paper is a supervised neural network for the fusion and denoising of multiscale STLF data. The model includes two parts: an encoder and a decoder [
25]. The regularization term is added to existing depth encoders to equip the model with sparse coding capabilities. SPL is a learning mechanism that was proposed in recent years in the field of ML; it is inspired by the learning processes of humans and animals, which operates from easy to difficult. SPL embeds the difficulty of course learning into the utilized optimization model while updating the model parameters based on the current sample ranking and updating the difficulty rankings of the samples based on the induced learning effect [
26]. The goal of SPL is to solve the problems of low model accuracy and convergence difficulty caused by sample quality problems [
27].
In summary, this article provides the following three main contributions:
The SPL strategy gradually incorporates samples into the developed model from simple to complex. This paper innovatively proposes combining SPL with the MLP method, which can effectively avoid local optimal solutions and further improve the prediction performance of the model.
To the best of our knowledge, AE–SPLMLP is the first multiscale power load forecasting model that incorporates a sparse deep autoencoder and the MLP method based on SPL into a computational framework.
The obtained experimental results show that compared with the support vector regression (SVR), gradient boosting decision tree (GBDT), extreme gradient boosting (XgBoost), light gradient boosted machine (LightGBM), and LSTM models, the AE–SPLMLP model achieves improvements of 6.88–99.66%.
The organization of the paper is as follows. In
Section 2, the factors that need to be considered in the study of power load forecasting are introduced, such as weather and temperature, as well as the data used in this paper. Then, a sparse deep autoencoder and the technique of combining SPL with the MLP method are described.
Section 3 presents the results of an experiment. The discussion and conclusion are provided in
Section 4.