# Busra Tas: Electricity Price Forecasting Using Hybrid Time Series Models

**Time: **
Fri 2019-05-24 13.00 - 14.00

**Location: **
Room 22, building 5, Kräftriket, Department of Mathematics, Stockholm University ￼

**Participating: **
Busra Tas

Abstract:

Accurate forecasting of hourly electricity prices is very important in a competitive market. Decision makers highly benefit from accurate forecasting. Because electricity cannot be stored, shocks to demand or supply affect electricity prices. As a result, electricity prices show high volatility. Additionally, it may have multiple levels of

seasonality. Therefore, forecasting with conventional methods is very difficult.

In this study, hybrid models are constructed with Seasonal Autoregressive Integrated Moving Average (SARIMA), TBATS and Neural Network models for the analysis of hourly electricity prices in Turkey. Time series can contain both linear and nonlinear patterns. Thus, using a hybrid model can give better results in forecasting. Both linear and nonlinear parts of the time series can be modeled by this approach. While SARIMA model and TBATS model are used to capture the linear behavior of the electricity price series. Neural Network is used to model the nonlinearity in the series. Electricity demand is used as an exogenous variable. Different combinations of hybrid models and individual models are compared in terms of forecasting performance. The results indicate that hybrid models mostly outperform the individual models in one-week ahead and one-day ahead forecasting.

Accurate forecasting of hourly electricity prices is very important in a competitive market. Decision makers highly benefit from accurate forecasting. Because electricity cannot be stored, shocks to demand or supply affect electricity prices. As a result, electricity prices show high volatility. Additionally, it may have multiple levels of

seasonality. Therefore, forecasting with conventional methods is very difficult.

In this study, hybrid models are constructed with Seasonal Autoregressive Integrated Moving Average (SARIMA), TBATS and Neural Network models for the analysis of hourly electricity prices in Turkey. Time series can contain both linear and nonlinear patterns. Thus, using a hybrid model can give better results in forecasting. Both linear and nonlinear parts of the time series can be modeled by this approach. While SARIMA model and TBATS model are used to capture the linear behavior of the electricity price series. Neural Network is used to model the nonlinearity in the series. Electricity demand is used as an exogenous variable. Different combinations of hybrid models and individual models are compared in terms of forecasting performance. The results indicate that hybrid models mostly outperform the individual models in one-week ahead and one-day ahead forecasting.