Abstract
It is very important to forecast the electric prices in deregulated markets for both producers and brokers. This information is crucial to make effective decisions concerning to production, purchase, maintenance and investment. In this study, we built two different systems for short-term prediction of electricity price in Turkish Electric Market. One of the systems built on ARIMA model, while the other employs a feed forward neural network. Both systems use calendar and historical price information as input. Performance of both systems are compared and it is shown that it is possible to forecast weekly electric price with an average error rate of %8.5.
Keywords
Autoregressive integrated moving average
Investment (military)
Turkish
Electricity
Production (economics)
Computer science
Electricity market
Time series
Econometrics
Operations research
Industrial organization
Economics
Microeconomics
Engineering
Electrical engineering
Machine learning
Subject Areas
Energy Load and Power Forecasting
·Electrical and Electronic Engineering ·Physical Sciences
Electric Power System Optimization
·Electrical and Electronic Engineering ·Physical Sciences
Market Dynamics and Volatility
·Economics and Econometrics ·Social Sciences
Citations by Year
2018: 2
2020: 1
2023: 2
2024: 1
2025: 1
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