A Stacking Learning Model Based on Multiple Similar Days for Short-Term Load Forecasting

Abstract

It is challenging to obtain accurate and efficient predictions in short-term load forecasting (STLF) systems due to the complexity and nonlinearity of the electric load signals. To address these problems, we propose a hybrid predictive model that includes a sliding-window algorithm, a stacking ensemble neural network, and a similar-days predictive method. First, we leverage a sliding-window algorithm to process the time-series electric load data with high nonlinearity and non-stationarity. Second, we propose an ensemble learning scheme of stacking neural networks to improve forecasting performance. Specifically, the stacking neural networks contain two types of networks: the base-layer and the meta-layer networks. During the pre-training process, the base-layer network integrates a radial basis function (RBF), random vector functional link (RVFL), and backpropagation neural network (BPNN) to provide a robust predictive model. The meta-layer network utilizes a deep belief network (DBN) and the improved broad learning system (BLS) to enhance predictive accuracy. Finally, the similar-days prediction method is developed to extract the relationship of electric load data in different time dimensions, further enhancing the robustness and accuracy of the model. To demonstrate the effectiveness of our model, it is evaluated using real data from five regions of the United States in three consecutive years. We compare our method with several state-of-the-art and conventional neural-network-based models. Our proposed algorithm improves the prediction accuracy by 16.08%, 16.83%, and 22.64% compared to DWT-EMD-RVFL, SWT-LSTM, and EMD-BLS, respectively. Empirical results demonstrate that our model achieves better accuracy and robustness compared with the baselines.

Covariance Matrix Adaptation Evolution Strategy for Improving Machine Learning Approaches in Streamflow Prediction

“…In recent years, hybrid machine learning models (MLMs) became preferred over standalone models and are successfully applied in the difference fields to model different variables; e.g., for load forecasting [31], to estimate the international airport freight volumes [32], to predict electricity prices [33], and for modeling of the tensile strength of the concrete [34]. Due to the nonlinear nature of hydrological variables' time series, researchers found more precise and accurate results by utilizing hybrid MLMs than standalone MLMs.…”

Hybrid Deep Learning Applied on Saudi Smart Grids for Short-Term Load Forecasting

“…Unlike the LSTM network, where only past information has an effect on output, this technique can be trained without the need for a predetermined input length. According to [33], two hidden layer nodes exist in the BiRNN architecture, and these layers are coupled to both the input and output layers. This means that the first hidden layer's recurrent unit is linked to previous time steps in a forward direction.…”

Respiratory Motion Prediction with Empirical Mode Decomposition-Based Random Vector Functional Link

“…This architectural choice enhances its overall generalization capabilities and significantly accelerates the learning speed. The effectiveness of these direct links within the RVFL network has been showcased across various domains, including time-series prediction [37,42], function approximation [43], as well as classification and regression tasks [19,[44][45][46]. In our prior studies [19], RVFL was employed on respiratory motion for multi-step ahead prediction.…”