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Forecasting Daily Electricity Market Price using Technical Analysis Indices and Support Vector Machine
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Maryam Rezaei   , Hossein Haroonabadi * , Ebrahim Khorram |
| Islamshahr Branch, Islamic Azad University, Islamshahr |
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Abstract: (1406 Views) |
| Forecasting the electricity price is important for electricity market players. Time series of the electricity price -as an inherently random phenomenon- have high uncertainty relative to the load. On the other hand, the non-stationary and non-linear characteristics of this time series make its forecasting difficult. On markets like the stock market, one can somehow forecast future price movements using technical analyses along with testing past prices and the volume of transactions. Therefore, this paper uses technical analysis indices for analyzing the time series data of the electricity market to forecast the electricity price. These indices are used as features extracted from time series of electricity price and applied to a Support Vector Machine (SVM) regression, through which the electricity price is predicted on daily horizon on Ontario electricity market. |
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| Keywords: Price forecasting, Electricity market, Technical analysis, Support Vector Machine. |
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Full-Text [PDF 389 kb]
(299 Downloads)
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Type of Study: Applicable |
Subject:
Special
Received: 2019/02/9 | Accepted: 2019/02/18 | Published: 2019/02/24
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