Vahid asgari - Mazandaran Electricity Distribution Company,Sari,Iran
Ali rahmani larmai - Mazandaran Electricity Distribution Company,Sari,Iran

Today s competitive world and economy is heavily dependent on electrical energy. Since electricity cannot be stored and producing more or less than the required needs may lead to some losses. The electric charge forecasting andpricing are considered as the main factors in planning and decision-making for future development plans and operationof power systems. In the future smart grids, electricity consumers will be able to react to changes in electricity prices.The total response of consumers to price could potentially shift the demand curve in the market. As a result, prices mayvary from original projections. In this paper, a forecasting framework is proposed that offers such dynamics in predicting the electricity price demand. In this framework, a mechanism based on principles of data mining data miningto determine the patterns in response to changes in consumer demand and prices are used. In this model, the weatherconditions (temperature and humidity), the days and special holidays are considered. And the results are expected to be done hourly, daily. Simulation results of the proposed method for forecasting demand and prices, which were obtained using the Australian electricity market, indicates that error is less compared to the previous methods.

Data mining, forecasting, demand and prices, smart grid, energy market