Abstract
In order to analyze the nature of electrical demand series in deregulated electricity markets, various forecasting tools have been used. All these forecasting models have been developed to improve the accuracy of the reliability of the model. Therefore, a Wavelet Packet Decomposition (WPD) was implemented to decompose the demand series into subseries. Each subseries has been forecasted individually with the help of the features of that series, and features were chosen on the basis of mutual correlation among all-time lags using an Auto Correlation Function (ACF). Thus, in this context, a new hybrid WPD-based Linear Neural Network with Tapped Delay (LNNTD) model, with a cyclic one-month moving window for a one-year market clearing volume (MCV) forecasting has been proposed. The proposed model has been effectively implemented in two years (2015–2016) and unconstrained MCV data collected from the Indian Energy Exchange (IEX) for 12 grid regions of India. The results presented by the proposed models are better in terms of accuracy, with a yearly average MAPE of 0.201%, MAE of 9.056 MWh, and coefficient of regression (R2) of 0.9996. Further, forecasts of the proposed model have been validated using tracking signals (TS’s) in which the values of TS’s lie within a balanced limit between −492 to 6.83, and universality of the model has been carried out effectively using multiple steps-ahead forecasting up to the sixth step. It has been found out that hybrid models are powerful forecasting tools for demand forecasting.
Highlights
In the present day electricity supply markets, the utility of load forecasting tool is high as it helps in managing the demand leading to a transparent price of electricity to the consumers
The performance of the proposed model was compared with standard benchmark stand-alone neural network (NN) models such as Feed Forward Neural Networks (FFNN), Genetic Algorithm (GA)-based NN (GANN), and Elman Recurrent Neural Networks (ERNN), along with conventional Wavelet Transform (WT)-based NN models
The hourly Indian Energy Exchange (IEX) unconstrained market clearing volume (MCV) data has been utilized for the evaluation of the performance measurement of presented load forecasting models
Summary
In the present day electricity supply markets, the utility of load forecasting tool is high as it helps in managing the demand leading to a transparent price of electricity to the consumers. For the forecasting of wind power, a pattern recognition-based hybrid method is proposed in which VMD is utilized for data processing, Gram-Schmidt Orthogonalization (GSO) is used for feature selection, and in the last step, the forecasting Extreme Learning Machine (ELM) was utilized for the training of each feature-based sub-series [28]. Advanced WT has been presented in which the entropy cost function is used to select the best wavelet basis for data decomposition, mutual information for feature selection, and neural networks for prediction of electricity load with a one and multi-step-ahead basis [33]. In this, the authors proposed a time series (statistical)-based forecasting model in which WPD is used as an input data pre-processing tool for MCV forecasting.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
