Abstract
Forecasting the electric load demand in advance is very important in deregulated market conditions to give proper production, purchase, maintenance and investment decisions. Correct price forecasts also depend on accurate load prediction. In this study, effects of calendar, historical price and load data on short-term load forecast for Turkish Deregulated Electricity market are tested using feed forward neural networks. The impact of each data on forecast performance is evaluated and best performance is obtained using a combination of historical and predicted load, calendar information and historical price information
Keywords
Electric power system
Electricity
Electricity market
Turkish
Investment (military)
Demand forecasting
Electrical load
Computer science
Production (economics)
Term (time)
Econometrics
Operations research
Power (physics)
Economics
Microeconomics
Engineering
Electrical engineering
Subject Areas
Energy Load and Power Forecasting
·Electrical and Electronic Engineering ·Physical Sciences
Electric Power System Optimization
·Electrical and Electronic Engineering ·Physical Sciences
Smart Grid Energy Management
·Electrical and Electronic Engineering ·Physical Sciences
Citations by Year
2018: 1
2019: 1
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