Implementation of Neural Networks and Feature Selection for Short Term Load Forecast

Veljanovski, Goran and Popovski, Pande and Atanasovski, Metodija and Kostov, Mitko (2022) Implementation of Neural Networks and Feature Selection for Short Term Load Forecast. 2022 57th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST).

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Abstract

Forecasting power demand is a significant factor in the planning and secure operation of power systems. Forecasting is based on considering all the factors that affect power demand. Reducing of the forecasting error can be achieved by identifying the factors that affect power demand, understanding their influence on the forecasting and adequate usage in the forecasting model. The selection of the optimal number and combination of features that will lead to a prediction model with less error is performed using the Recursive Feature Elimination with Cross- Validation (RFECV) elimination method. The features are selected according to a statistical importance measure. Then, the Artificial Neural Networks (ANN) are used to build load forecasting models, for a specified hour in future, and then, by combining those models, an one day-ahead model was created. The proposed approach combines the abilities of the neural networks to learn the nonlinear relationship between features and the optimization capability of the RFECV elimination method to find the best model for forecasting.

Item Type: Article
Subjects: Scientific Fields (Frascati) > Engineering and Technology > Electrical engineering, electronic engineering,information engineering
Scientific Fields (Frascati) > Engineering and Technology > Other engineering and technologies
Divisions: Faculty of Technical Sciences
Depositing User: Prof Mitko Kostov
Date Deposited: 26 Jul 2022 11:09
Last Modified: 26 Jul 2022 11:09
URI: http://eprints.uklo.edu.mk/id/eprint/7016

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