Optimal Decomposition and Reconstruction of Discrete Wavelet Transformation for Short-Term Load Forecasting

Abstract

To achieve high accuracy in prediction, a load forecasting algorithm must model various consumer behaviors in response to weather conditions or special events. Different triggers will have various effects on different customers and lead to difficulties in constructing an adequate prediction model due to non-stationary and uncertain characteristics in load variations. This paper proposes an open-ended model of short-term load forecasting (STLF) which has general prediction ability to capture the non-linear relationship between the load demand and the exogenous inputs. The prediction method uses the whale optimization algorithm, discrete wavelet transform, and multiple linear regression model (WOA-DWT-MLR model) to predict both system load and aggregated load of power consumers. WOA is used to optimize the best combination of detail and approximation signals from DWT to construct an optimal MLR model. The proposed model is validated with both the system-side data set and the end-user data set for Independent System Operator-New England (ISO-NE) and smart meter load data, respectively, based on Mean Absolute Percentage Error (MAPE) criterion. The results demonstrate that the proposed method achieves lower prediction error than existing methods and can have consistent prediction of non-stationary load conditions that exist in both test systems. The proposed method is, thus, beneficial to use in the energy management system.

Framework Integrating Lossy Compression and Perturbation for the Case of Smart Meter Privacy

“…A WT (wavelet transform) can orthogonally decompose a time series into scaling and wavelet coefficients [35,36]. The compression ratio depends on the deletion of trivial data points and it changes due to the DW (Daubechies' wavelets), thresholds and levels of decomposition (LoD).…”

Intelligent Systems for Power Load Forecasting: A Study Review

“…Aprillia et al have constructed a system for power load prediction as follows: a whale optimization algorithm to detect and choose the appropriate level of the wavelet decomposition, discrete wavelet transform to decompose data into detail and approximation signals and a multiple linear regression technique to predict the final result of the load. The proposed scheme was tested for weekdays and holiday days for all seasons and produced a low forecasting error when compared with different models [42]. Amral et al have designed multiple linear regression for load forecasting.…”

PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting

“…PLS is a straightforward dimensionality reduction technique that maps the variables in a new feature space with lower dimensions. The Variable Importance of load Patterns (VIP) for 32 features is shown in Regarding Figure 9, the most important features are hour, workday, temperature and lagged load (t − x) with x ∈ [1,2,3,4,5,6,7,11,12,13,17,18,19,20,21,22,23]. Thus, the selected threshold is VIP = 0.5.…”

“…The DL architectures can automatically carry out the data processing. However, there are very few available research works oriented towards the hybridization of DL models along with different features engineering techniques to achieve higher prediction accuracy [20,21]. Therefore, this paper aims to tackle the non-linearity and volatility of STLF by coupling data augmentation, variable importance analysis, and feature smoothing alongside with hybrid CBiLSTM model with the aim to provide a highly reliable hourly STLF system.…”