Load Forecasting Based on LVMD-DBFCM Load Curve Clustering and the CNN-IVIA-BLSTM Model

Abstract

Power load forecasting plays an important role in power systems, and the accuracy of load forecasting is of vital importance to power system planning as well as economic efficiency. Power load data are nonsmooth, nonlinear time-series and “noisy” data. Traditional load forecasting has low accuracy and curves not fitting the load variation. It is not well predicted by a single forecasting model. In this paper, we propose a novel model based on the combination of data mining and deep learning to improve the prediction accuracy. First, data preprocessing is performed. Second, identification and correction of anomalous data, normalization of continuous sequences, and one-hot encoding of discrete sequences are performed. The load data are decomposed and denoised using the double decomposition modal (LVMD) strategy, the load curves are clustered using the double weighted fuzzy C-means (DBFCM) algorithm, and the typical curves obtained are used as load patterns. In addition, data feature analysis is performed. A convolutional neural network (CNN) is used to extract data features. A bidirectional long short-term memory (BLSTM) network is used for prediction, in which the number of hidden layer neurons, the number of training epochs, the learning rate, the regularization coefficient, and other relevant parameters in the BLSTM network are optimized using the influenza virus immunity optimization algorithm (IVIA). Finally, the historical data of City H from 1 January 2016 to 31 December 2018, are used for load forecasting. The experimental results show that the novel model based on LVMD-DBFCM load c1urve clustering combined with CNN-IVIA-BLSTM proposed in this paper has an error of only 2% for electric load forecasting.

A Combined Method for Short-Term Load Forecasting Considering the Characteristics of Components of Seasonal and Trend Decomposition Using Local Regression

“…. , x n ]; and K(x, x) = k(x i , x j ) n×n is a covariance matrix, where each matrix element is computed through the kernel function, which is specifically represented by formula (8). I n is an n-dimensional identity matrix, while σ 2 n I n represents the covariance matrix of the noise.…”

“…In this GPR model, σ 2 n is a parameter, and in formula (8), there are also parameters σ 2 and l. These parameters can all be obtained by maximizing the marginal likelihood function. Denoting all the parameters in the kernel function as θ = [σ 2 , l], the logarithm of the marginal likelihood (LML) is given by formula (12).…”

“…With the advancement of intelligent algorithms, some electricity load forecasting methods based on these algorithms have emerged. These algorithms comprise support vector machines (SVMs) [5], long short-term memory (LSTM) neural networks [6], gated recurrent units (GRUs) [7], and convolutional neural networks (CNNs) [8], which are adept at handling the nonlinear relationships and complex features of the load. These methods can effectively handle nonlinearity and multidimensional data, but they are sensitive to outliers, and facing data mutations or breakpoints can lead to performance degradation [9].…”

Long-term Load Forecasting for Optimal Power System Planning and Decision-Making

“…To increase the accuracy of load forecasting for power systems, Hu et al [10] proposed a new model based on data mining and deep learning that takes into account historical load data, weather information, date types, real-time electricity prices, etc. This is accomplished by combining the load curve clustering DBFCM method, feature fusion extraction CNN, and parameter optimisation IVIA-BLSTM.…”

Electric Load Forecasting Based on Deep Ensemble Learning

“…Convolutional neural network layers are utilized to extract local features from time series data [21]. Through convolution operations and activation functions, CNN can effectively capture the spatial and temporal correlations in the input data.…”