• M. Madan Gopi
• Dr. S. Farook

Electricity load and price forecasting are two important factors required in a restructured power system. The performance of demand response is probably to be decreased in the absence of accurate load and price forecasting. In this paper, a hybrid load and price forecasting model based on hybrid time series and Adaptive Wavelet Neural Network (AWNN) models is proposed, in which Autoregressive Integrated Moving Average (ARIMA) model is used to process the non-stationary data, the Generalized Conditional Heteroscedastic (GARCH) model is used to capture the dynamics of a time series conditional variance and AWNN presents non-linear impacts. The Mean Absolute Percentage Error (MAPE) evaluates the accuracy of forecasting results. To demonstrate the effectiveness of the proposed method, day-ahead prediction of load and price in New York independent system operator (NYISO) market for north and west zone is considered.

Load and price forecasting, MAPE, ARIMA, GARCH and AWMN models

"FORECASTING SHORT-TERM LOAD AND PRICE IN RESTRUCTURED POWER SYSTEM USING HYBRID MODEL", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 6, page no.457-464, June-2018, Available :http://www.jetir.org/papers/JETIR1806758.pdf

"FORECASTING SHORT-TERM LOAD AND PRICE IN RESTRUCTURED POWER SYSTEM USING HYBRID MODEL", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 6, page no. pp457-464, June-2018, Available at : http://www.jetir.org/papers/JETIR1806758.pdf