Forecasting of Market Clearing Volume Using Wavelet Packet-Based Neural Networks with Tracking Signals

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.

Embedding the weather prediction errors (WPE) into the photovoltaic (PV) forecasting method using deep learning

“…It helps prosumers to optimize energy consumption by scheduling appliances at hours when the solar energy is available according to the day-ahead prediction (Almeida et al, 2015), (Oprea & Bâra, 2023). It also assists them to create bids and offer the surplus on the local markets or cover the deficit by acquiring energy from the same local market where the prices are usually lower (Saroha et al, 2021), (Oprea & Bâra, 2021). Furthermore, the PVF has a major impact on the Energy Communities (EC) at the regional level where the integrated energy sources, including medium and large PV systems, storage facilities and generating sources operate to locally supply EC from distributed generation rather than grid.…”