Research on Power Load Forecasting Using Deep Neural Network and Wavelet Transform

Introduction

The development of the electric power industry is the basis of the industrial development of the whole country, which is related to national security, social stability, and people’s livelihood, and is an indispensable pillar industry in modern society. As a special kind of energy, the production and consumption of electricity are simultaneously completed, which makes it impossible to store. Thus, a dynamic balance between the production and consumption of electricity must be maintained at all times, which neither allows the supply to exceed the demand, which wastes energy resources and causes transmission blockage, nor allows the supply to exceed the demand, which cannot guarantee the users’ demand for electricity and thus induces power shortage and large area blackout (Lin & Luan, 2020). To ensure the balance between supply and demand of electric energy, power load forecasting came into being. Through analyzing the historical load, predicting the future load situation of electricity, and formulating generation, transmission and power supply plans in advance based on the prediction results can achieve the balance of supply and demand in the power system to the greatest extent and ensure the stability and quality of power supply in the power system (Kosowski et al., 2019). Therefore, the prediction of regional load in a certain period or several periods of time is extremely significant for the development of regional power industry. Currently, the power data are growing exponentially, the scale of power grid is getting bigger and bigger, the complexity of power data is getting higher and higher, the factors affecting the load are becoming more and more diversified. Also, social, political, weather, and even economic factors have become the background of power load prediction, and the traditional means of power load prediction are hardly applicable to the prediction analysis in today’s complex background (Kumar et al., 2022). A comprehensive and accurate load forecasting is the key for the power system to be able to operate safely and regulate flexibly, so it is particularly important to study an algorithm with high accuracy in load forecasting and that can take more external factors into account. Traditional statistical load forecasting models have a large data dependency, small-scale data cannot be trained perfectly, and large-scale data are time-consuming and labor-intensive to process. Therefore, how to build an algorithm that is fast, while taking into account the scale of the data, becomes an urgent problem (Meng et al., 2022).

The first step is to start from the data themselves, which have certain patterns, but it is difficult to distinguish the specific forms of different parts of the data from traditional regression and statistical theories alone. As a result, further decomposition of the data is needed. In this study, the authors took into account more information such as weather, economy, and service life, as predictors, but it is difficult to count such complex data for most regions, so starting the analysis from existing data is a vital way to enhance data performance (Deng et al., 2019). With the development of signal processing technology, modern signal processing methods such as wavelet variation and empirical modal decomposition have greatly enhanced the performance of the data in the frequency domain. Therefore, the selection of such methods for data denoising and frequency domain feature extraction can greatly enhance the data performance. As for the selection of prediction methods, machine learning type methods have become the main research object for power prediction when the computer performance has not been significantly improved (Rhif et al., 2019). Nowadays, the development of deep learning has greatly increased the interest of the academic community in the application of deep learning to prediction. Deep neural networks ensure the model accuracy and robustness through complex network structure, which greatly improves the prediction accuracy (Emmert-Streib et al., 2020). To enhance the signal performance and improve the result, the wavelet transform is also used to extract the frequency feature. Therefore, in this paper the authors use the combination of the wavelet transform and deep learning to complete the electric load prediction. The contributions of this paper are as follows: