Application of a new hybrid forecast engine with feature selection algorithm in a power system. Issue 5 (4th July 2019)

• Record Type: Journal Article
• Authors: Ghadimi, Noradin; Akbarimajd, Adel; Shayeghi, Hossein; Abedinia, Oveis
• Is Part Of: International journal of ambient energy. Volume 40:Issue 5(2019)
• Page Start: 494
• Page End: 503
• Publication Date: 2019-07-04
• Subjects: Forecast model -- feature selection -- forecast engine -- hybrid neural network; Power resources -- Periodicals; Renewable energy sources -- Periodicals; 621.04205
• DOI: 10.1080/01430750.2017.1412350
• Languages: English
• ISSNs: 0143-0750

Abstract

ABSTRACT: To operate a power system effectively, an accurate prediction model is demanded. So, short-term load forecast is one of the major discussions in deregulated power markets. This prediction model needs a strong and accurate method to tackle the complexity, non-stationary and volatility of this signal. Hence, a new hybrid forecasting model is proposed in this paper, to solve the load forecast requirement. The proposed structure consists of a three-stage Neural Network-based forecast engine with different learning algorithms. Also, the input signal of this forecast engine is filtered by a new feature selection model to find the high relevancy and low redundancy of features. The proposed strategy is implemented and tested on real-world engineering data through a comparison with other techniques. The numerical results obtained demonstrate the validity of the proposed method.