1. Introduction
Electric load forecasting acts an important part in power station operations, such as the expansion of power generation, dispatch scheduling of generation production, maintenance, and the insurance of continuously supplied electric power [
1]. An accurate electric load forecasting model can not only assist the power station in the management of electricity and the arrangement of operations, but is also able to reduce the loss of auxiliary power, which enhances the stability and the economic benefits of the station. Inaccurate forecasting results, on the other side, can result in abundant electricity waste. For example, underestimated forecasting will raise the difficulty in supplying adequate electric resources, which raises the cost of the power station [
2]. Therefore, to obtain a satisfying prediction result, the need to develop an accurate and effective electric load forecasting system is intensely high.
To obtain an accurate forecasting result for electric power stations, many short-term predicting methods were introduced, and those can mainly be classified into three categories: conventional methods, modern methods, and hybrid methods. Firstly, conventional methods include multi-linear regression analysis, time series, state space models, general exponential smoothing, and knowledge-based methods [
3,
4,
5,
6,
7,
8]. However, these methods cannot provide appropriate nonlinear exiting mathematical relationships to express actual electric loads. Secondly, modern methods are intelligent evolutionary algorithms, expert systems, neural networks, and fuzzy inference [
9,
10,
11,
12,
13,
14,
15,
16,
17]. Intelligent algorithms and neural networks can obtain good performance because of their clear patterns, easy implementation, and strong ability to address the problem. Thirdly, hybrid forecasting methods, which are proposed to avoid the shortcomings in individual methods, have become more and more popular [
18,
19].
The procedure of traditional forecasting methods is precise, which are based on traditional mathematic ideas and theories, such as statistics, calculus, and modeling [
20]. The main theory of trend extrapolation technology is to find the patterns in the given data to forecast future data based on the trend equation. Though the method is simple, it can obtain an accurate forecasting result for continuous electric load data. However, its accuracy can be greatly impacted by random load components, which is an inadequacy of the method [
21]. Regression analysis is usually used in short-term electric data prediction [
22]. This method has many advantages, such as a simple principle and a better quality of data, which leads to better precision. However, the selection of the main factors affecting power load in the model is difficult as many factors that affect the forecasting accuracy are hard to quantify. This model is lacking any self-study capability and the input variable and output variable cannot be revised automatically [
23]. With years of development, the time series forecasting method has become a mature theory method and has been applied to power load forecasting [
24]. The basic time series prediction models mainly include AR (auto regressive), MA (moving average), and ARMA (auto regressive moving average) [
25]. Although the time series forecasting method has advantages, such as only requiring a small volume of historical data and small amounts and fasts speed of calculation, this method has certain limitations, such as its inability to reflect the influence of meteorological factors and how its forecasting accuracy will decrease with the increase of the prediction step [
26]. An ANN (Artificial Neural Network) is a type of nonlinear simulation of the human brain information processing system with an intelligent processing process for an inaccurate variation trend, this method also has a good ability to adapt, is able to grasp information and keep on learning, and has good knowledge reasoning and self-optimization [
27]. An expert system is a computer system based on the knowledge of the programming approach, mainly a software system, and the main components of an expert system include the inference engine of the system, the expert knowledge base, the explain interface, and the knowledge acquisition module. It is a program that has decision making capabilities based on reasoned knowledge, however, this method is limited by whether the expert knowledge is complete [
28]. The grey forecasting method is an important technique in grey theory, which uses approximate differential equations to describe future tendencies for a time series [
29]. Limitation of this method is that the greater the dispersion degree of data, the worse the forecasting accuracy. Although traditional forecasting methods and forecasting methods based on intelligent computing have their respective applications, it is difficult to obtain high accuracy prediction when utilizing one of them by itself [
30]. In the literature related to forecasting [
31,
32,
33], the forecasting results are not quite as good with any single forecasting model. The primary reason is that single forecasting models cannot extract the complicated factors in reality.
Recently, to overcome drawbacks of conventional methods, researchers focused more on hybrid or combined approaches. These approaches are usually made up of data pre-processing techniques, parameter optimization algorithms, and weighting-combined methods [
34,
35,
36,
37,
38,
39,
40,
41], which can enhance the accuracy of short-term electric load forecasting. For example, Wang et al introduced a combined approach using GPR (Gaussian process regression) and WT (wavelet transform) to combine individual models and identify noise in the original time series, the results of which indicated that the forecasting accuracy is improved with the combined model applied [
34]. Liu et al utilized EMD (empirical mode decomposition) to decompose original data into IMFs (intrinsic mode function) for reconstructing a more stationary time series [
40]. The performance of the model was verified by four case studies. A combined approach which applies BPNN (back propagation neural network), ARIMA (autoregressive integrated moving average model), and ANFIS (adaptive network-based fuzzy inference system) with optimized weights is proposed by Yang et al, which was also proved for higher accuracy by case studies [
41]. Those discussed methods illustrated that hybrid and combined models can improve the effectiveness of forecasting, compared with conventional models [
42,
43,
44].
Through the review of previous articles, the forecasting methods mentioned earlier all have several shortcomings. The drawbacks of those approaches are summed up as the following: (1) Traditional statistical methods cannot perform forecasting using data with high noise and fluctuation features, which electric load data usually have. This method is limited by the assumption of a smooth linear form time series. Furthermore, these traditional methods require great amounts of historical data to train for predicting, which shows the fact that these methods depend highly on a raw time series. When a raw time series changes unexpectedly, because of certain factors in the power stations, the accuracy of the forecasting will be relatively low [
45]. (2) Artificial intelligence methods, which can be successfully utilized in forecasting, can capture non-linear features in raw data [
46]. However, this method still has many drawbacks, such as a local optimum, over-fitting, and low convergence rate [
47]. (3) Individual methods rarely focus on the data preprocessing technique, so they usually obtain a relatively low forecasting accuracy. Therefore, due to these disadvantages of conventional methods mentioned above, a hybrid approach, which can capture the hidden features in the electric load data and can be widely applied, needs extreme consideration in order to achieve accurate forecasting results. [
48]. This paper proposes a new hybrid approach combining ensemble empirical mode decomposition (EEMD), a Whale Optimization Algorithm (WOA), and a support vector machine (SVM).
The leading progress of this paper, in comparison with other works in the field of electric load prediction, is summarized as follows:
The method introduced in this paper utilized a data preprocessing technique to improve the accuracy of the proposed approach. Raw electric load data is first broken down into sub-signals. Signals with high noise are taken away and the rest are reorganized into a more stationary series. In this way, the uncertainty and irregularity in the electric load data can be decreased and features in the electric load data can be better analyzed. Eventually, the performance of the proposed method is enhanced.
The new model utilizes Whale Optimization to optimize key parameters in SVM before forecasting. Whale Optimization has the advantage of requiring few parameters and a strong problem-solving ability, which is an effective tool in global optimization. By using it, a support vector machine can greatly improve its predicting accuracy and avoid shortcomings in traditional approaches, such as dimensionality, local optima, and over-learning.
To further test the performance of the hybrid model, traditional and hybrid models are used for comparison in the experiment. Comprehensive evaluations are applied, which include multi-step ahead forecasting performance evaluation metrics, such as error indexes, DM tests, and forecasting effectiveness, verifying the ability of the proposed model.
The proposed approach can be an effective tool for an electric station. Experiments concluded in this paper are based on electric load data from two different power stations with two different time horizons. The results showed that the hybrid approach can enhance the accuracy of forecasting and is easily applied in different stations.
This paper is organized as follows.
Section 2 introduces the required techniques and the proposed approach.
Section 3 and
Section 4 introduces the evaluation criterion and description of the experiment data sets. For
Section 5,
Section 6, and
Section 7, experiments were conducted and the results and the significance of the hybrid model are analyzed in detail. Finally,
Section 8 concludes the paper and gives a possible direction for future work.
8. Conclusions
Electrical load prediction has become more and more important in the arranging of economic development, both nationally and regionally, especially in developing areas with high electricity consumption and demand. Accurate electric load forecasting can not only help executives in power grid management, which can satisfy requirements of daily planning, but also to avoid unnecessary risks and costs, which improves the security and the economic competitiveness of the power station. However, relevant works in the field of electricity generation, distribution, and consumption are still not satisfying, though they contribute significantly to the area of electric load forecasting. Moreover, uncertainty factors of the electric load data, such as high fluctuation, autocorrelation, and so on, make the work of forecasting rather challenging. This paper proposed a hybrid approach and testified the effectiveness of it by comparing with three traditional methods (BPNN, RBFNN, and ARIMA) and two hybrid models (EMD-PSO-BPNN and EMDCSOWNN), using a data preprocessing technique (EEMD) or not and comparing them with state-of-the-art optimization algorithms (CSO and PSO). Furthermore, to further verify the performance and the adaptability of the proposed approach for electric load forecasting, two different data sets from sperate power stations are applied in this study. The Diebold-Mariano test and forecasting effectiveness are also applied to test the forecasting ability of the proposed approach. Overall, experimental results suggest that the proposed approach can not only perform accurate electric load forecasting, but may also be easily adapted in different electric power stations. The limitation of this paper is that, based on the work of Moghram and Rahman [
62], the proposed model can not achieve the same high accuracy for all the data. The possible direction for future work is to combine the advantage of artificial intelligence and existing forecasting models to build a more accurate and effective approach for electric load prediction.
In conclusion, the newly established hybrid model can perform accurate electric load forecasting, which is a key factor for building an effective smart grid system that can provide an appropriate supply of electric power. The experimental results suggest its high accuracy and adaptability make it possible to be utilized in many considerable fields, especially in smart energy systems.