Short-Term Load Probabilistic Forecasting Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Reconstruction and Salp Swarm Algorithm

Abstract

Short-term load forecasting is an important part of load forecasting, which is of great significance to the optimal power flow and power supply guarantee of the power system. In this paper, we proposed the load series reconstruction method combined improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) with sample entropy (SE). The load series is decomposed by ICEEMDAN and is reconstructed into a trend component, periodic component, and random component by comparing with the sample entropy of the original series. Extreme learning machine optimized by salp swarm algorithm (SSA-ELM) is used to predict respectively, and the final prediction value is obtained by superposition of the prediction results of the three components. Then, the prediction error of the training set is divided into four load intervals according to the predicted value, and the kernel probability density is estimated to obtain the error distribution of the training set. Combining the predicted value of the prediction set with the error distribution of the corresponding load interval, the prediction load interval can be obtained. The prediction method is verified by taking the hourly load data of a region in Denmark in 2019 as an example. The final experimental results show that the proposed method has a high prediction accuracy for short-term load forecasting.

Nonlinear Trend and Multiscale Variability of Dry Spells in Senegal (1951–2010)

“…Compared to standard decomposition methods such as Fourier transform and wavelet analysis, ICEEMDAN is preferred for its intuitive, posteriori, direct, empirical, and adaptive nature, eliminating the need for predetermined basis functions [49,52]. Moreover, ICEEMDAN incorporates several improvements over classical EMD techniques [53][54][55][56][57].…”

Emerging Trends in Energy Economics

“…Christopoulos et al in [5] investigated the effect that the COVID-19 pandemic and the stock market volatility have on oil price volatility. Three papers apply various forecasting techniques in forecasting energy: Gupta and Pierdzioch in [6] are forecasting the volatility of crude oil using the LASSO estimator, Hu et al in [7] forecast the Short-Term Load using the Ensemble Empirical Mode Decomposition coupled with the Salp Swarm Algorithm, and Mouchtaris et al in [8] forecast the Natural Gas Spot Prices using an arsenal of Machine Learning Methodologies. The Special Issue is concluded by two review papers: Menegaki in [9] summarizes and compares results of different studies in the energy-sustainable growth nexus for various groups of countries, and Oliveira and Moutinho in [10] perform a bibliographic analysis on the topics of renewable energy, economic growth and the economic development nexus.…”