• A.YUVA KISHORE
• N.MOUNIKA
• R.BALAJI
• R.SASI KUMAR
• M.GOWTHAMI

By the marketization of Electricity, Electric energy became a necessary commodity. To enjoy the optimum benefits, market participants should have control on electricity prices and accurate price forecasting helps safe economic dispatch and reliable operation. But due to complex features like high volatility, high frequency, nonlinearity, mean reversion and non-stationary that make forecasting very difficult. Many models were proposed for electricity price forecasting and each model have their improved features. This model proposes a new model which is an extension for Autoregressive Moving Average (ARMA),which includes exogenous variables i.e.,ARMAHX model. Here we uses functional time series model which accounts for autoregressive and Moving average effects. The structure of the proposed model is a linear regression where functional parameters operate on functional variables. The variables can be lagged values of the series (autoregressive terms), past observed innovations (moving average terms) or exogenous variables. Quasi-Newton algorithm is used to minimize the Squared errors. The proposed algorithm is tested with daily electricity price profile of German market

Electricity price Forecasting, Functional time series, Autoregressive models,ARMA

"Electricity price forecasting in day-ahead market by ARMAHX model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.193-200, March-2019, Available :http://www.jetir.org/papers/JETIR1903J32.pdf

"Electricity price forecasting in day-ahead market by ARMAHX model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp193-200, March-2019, Available at : http://www.jetir.org/papers/JETIR1903J32.pdf