The forecast of electricity price has become an important aspect of the electricity market due to competitive business environment. Power producers and consumers are bound to determine a precise price forecasting because this information is vital for decision making process and directly linked with company profit. Decisions, regarding optimal scheduling of generators, bidding strategies and demand side management are based on price forecast. In recent years, development of new approaches for short term price forecasting has captured the interest of the researchers. The electricity price forecasting is difficult due to its volatile nature. Moreover, the electricity can't be stock piled like other commodities. With these two issues, forecasting of electricity price has become a daunting task to perform for system planners and designers. This paper presents a comparative study of time series methods namely Decomposition, Moving Average and Trend analysis methods to forecast the electricity price. A meaningful comparison of forecasting results is presented on the basis of standard error indices .
Historical data, Time seriesForecasting, Electricity price