Mid- to Long-Term Electric Load Forecasting Based on the EMD–Isomap–Adaboost Model

Abstract

Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. In this study, a hybrid algorithm (EMDIA) that combines empirical mode decomposition (EMD), isometric mapping (Isomap), and Adaboost to construct a prediction mode for mid- to long-term load forecasting is developed. Based on full consideration of the meteorological and economic factors affecting the power load trend, the EMD method is used to decompose the load and its influencing factors into multiple intrinsic mode functions (IMF) and residuals. Through correlation analysis, the power load is divided into fluctuation term and trend term. Then, the key influencing factors of feature sequences are extracted by Isomap to eliminate the correlations and redundancy of the original multidimensional sequences and reduce the dimension of model input. Eventually, the Adaboost prediction method is adopted to realize the prediction of the electrical load. In comparison with the RF, LSTM, GRU, BP, and single Adaboost method, the prediction obtained by this proposed model has higher accuracy in the mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE), and determination coefficient (R2). Compared with the single Adaboost algorithm, the EMDIA reduces MAE by 11.58, MAPE by 0.13%, and RMSE by 49.93 and increases R2 by 0.04.

Research on transformer fault diagnosis based on ISOMAP and IChOA‐LSSVM

“…Feature extraction of raw transformer fault data using Isomap requires the determination of two important parameters, the number of nearest neighbour points k and the number of low dimensional space dimensions d [28]. If the value of k is large it will increase the running time of the model.…”

Short-Term Electrical Load Forecasting Based on Neural Networks

“…On the one hand the use of various energy sources in one system makes it possible to instantly respond to changes in consumption in its individual sections, but also complicates its management. In order for the energy system to work effectively, it is necessary to apply accurate methods for forecasting the consumption of electrical energy [2]. Currently, one of the urgent tasks is the development of methods and appropriate programs for forecasting power consumption with a high degree of accuracy.…”

High Precision Modeling of Power Demand for Electricity Distribution Companies with frequent load shedding

“…Load forecasting is an important aspect of the energy industry, because it allows for the e cient management of power generation and distribution. It is an important link required for optimizing and dispatching power systems [7]. Essentially, timely and accurate forecasting methods can provide both instant, miscellaneous, and auxiliary decision support for power supplies and power grids [7].…”

“…It is an important link required for optimizing and dispatching power systems [7]. Essentially, timely and accurate forecasting methods can provide both instant, miscellaneous, and auxiliary decision support for power supplies and power grids [7]. Also, it is of major signi cance to the projection and formulation of the power allocation process and planning, and reduction of operating costs while ensuring reliable power production [23].…”

“…Corporations are consistently facing the replacement of equipment for power generation as a result of constant marketing and as a result of this, hence, power load forecasting model with higher forecasting accuracy becomes essential for grid operation with a more meteorological dataset as a complement to existing meteorological data set for improving power forecast [7]. However, this study aims at short-term load forecasting which requires hourly to daily power demand for 4 years.…”