A New Hybrid Model Based on Data Preprocessing and an Intelligent Optimization Algorithm for Electrical Power System Forecasting

Abstract

The establishment of electrical power system cannot only benefit the reasonable distribution and management in energy resources, but also satisfy the increasing demand for electricity. The electrical power system construction is often a pivotal part in the national and regional economic development plan. This paper constructs a hybrid model, known as the E-MFA-BP model, that can forecast indices in the electrical power system, including wind speed, electrical load, and electricity price. Firstly, the ensemble empirical mode decomposition can be applied to eliminate the noise of original time series data. After data preprocessing, the back propagation neural network model is applied to carry out the forecasting. Owing to the instability of its structure, the modified firefly algorithm is employed to optimize the weight and threshold values of back propagation to obtain a hybrid model with higher forecasting quality. Three experiments are carried out to verify the effectiveness of the model. Through comparison with other traditional well-known forecasting models, and models optimized by other optimization algorithms, the experimental results demonstrate that the hybrid model has the best forecasting performance.

Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark

“…1. the dataset used for testing and evaluation is not defined [31][32][33][34][35][36][37]; 2. the dataset used for training is not defined [21,33,35,41,42]; 3. the inputs of the model are unclear [35,36,[38][39][40]; 4. the selection of hyperparameters is unclear [13, 21, 37, 48, 79, 82, 91-93, 96, 99].…”

“…• Some of the existing papers do not provide enough details to reproduce the research. The three most common issues are: (i) not specifying the exact split between the training and test dataset [31][32][33][34][35][36][37], (ii) not indicating the inputs used for the prediction model [35,36,[38][39][40], and (iii) not specifying the dataset employed [21,33,41,42]. This obviously prevents other researchers from validating the research results.…”