Long-Term Price Forecasting for the Cost-Benefit Analysis of Power Equipment

2018 IEEE 59th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON 2018): Conference Proceedings 2018
Ļubova Petričenko, Antans Sauļus Sauhats, Romāns Petričenko, Deniss Bezrukovs

Forecasting of electricity price plays a significant role in electrical network planning and development. In the present paper, we offer three prediction approaches: the naïve method (NM), Fourier transformation (FT) with imposition of white noise and the artificial neural network (ANN) model. Our research proves the possibility of using any of three approaches due to the high forecasting accuracy of all of them. A case study using three types of forecasting methods, real-life data and a model of the distribution grid of our native country is presented to demonstrate the efficiency of our investigation and used to estimate the income generated by the energy storage system.

Keywords
Electricity price, forecasting, naïve method, Fourier transformation, ANN
DOI
10.1109/RTUCON.2018.8659888
Hyperlink
https://ieeexplore.ieee.org/document/8659888

Petričenko, Ļ., Sauhats, A., Petričenko, R., Bezrukovs, D. Long-Term Price Forecasting for the Cost-Benefit Analysis of Power Equipment. In: 2018 IEEE 59th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON 2018): Conference Proceedings, Latvia, Riga, 12-14 November, 2018. Piscataway: IEEE, 2018, pp.1-5. ISBN 978-1-5386-6904-4. e-ISBN 978-1-5386-6903-7. Available from: doi:10.1109/RTUCON.2018.8659888

Publication language
English (en)