Load and electricity prices forecasting using Generalized Regression Neural Networks

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

José Pedro Paulos (Author) Other; José Nuno Fidalgo (Author) FEUP

Conference proceedings International

Title: 2018 International Conference on Smart Energy Systems and Technologies, SEST 2018 - Proceedings

Pages: 1-6

2018 International Conference on Smart Energy Systems and Technologies, SEST 2018

10 September 2018 through 12 September 2018

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 1 Citation

INSPEC

Other information

Authenticus ID: P-00P-W7Q

DOI: 10.1109/sest.2018.8495726

Abstract (EN): Over time, the electricity price and energy consumption are increasingly growing their weight as prime foundations of the electrical sector, with their analysis and forecasts being targeted as key elements for the stable maintenance of electricity markets. The advent of smart grids is escalating the importance of forecasting because of the expected ubiquitous monitoring and growing complexity of a data-rich ever-changing milieu. So, the increasing data volatility will require forecasting tools able to rapidly readjust to a dynamic environment. The Generalized Regression Neural Network (GRNN) approach is a solution that has recently re-emerged, emphasizing good performance, fast run-times and ease of parameterization. The merging of this model with more conventional methods allows us to obtain more sturdy solutions with shortened training times, when compared to conventional Artificial Neural Networks (ANN). Overall, the performance of the GRNN, although slightly inferior to that of the ANN, is suitable, but linked to much lower training times. Ultimately, the GRNN would be a proper solution to blend with the latest smart grids features, which may require much reduced forecasting training times.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6