Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market

Abstract

Load information plays an important role in deregulated electricity markets, since it is the primary factor to make critical decisions on production planning, day-to-day operations, unit commitment and economic dispatch. Being able to predict the load for a short term, which covers one hour to a few days, equips power generation facilities and traders with an advantage. With the deregulation of electricity markets, a variety of short term load forecasting models are developed. Deregulation in Turkish Electricity Market has started in 2001 and liberalization is still in progress with rules being effective in its predefined schedule. However, there is a very limited number of studies for Turkish Market. In this study, we introduce two different models for current Turkish Market using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Artificial Neural Network (ANN) and present their comparative performances. Building models that cope with the dynamic nature of deregulated market and are able to run in real-time is the main contribution of this study. We also use our ANN based model to evaluate the effect of several factors, which are claimed to have effect on electrical load.

Time Series Models for Short Term Prediction of the Incidence of Japanese Encephalitis in Xianyang City, P R China△

“…These include general regression models, Markov chain models, Seasonal Autoregressive Integrated Moving Average (SARIMA), Grey models, etc. [9][10] Due to the strong seasonality of JE, SARIMA model is the most appropriate model. SARIMA model is a statistical method and prediction approach that is particularly useful if there is time dependence in each observation.…”

SARIMA Modelling for Forecasting the Electricity Consumption of a Health Care Building

“…Erasmo Cadenas et al [13] in 2016 compared univariate ARIMA model to multivariate NARX model, after which it was concluded that NARX model is performing better. Omer Ozgur Bozkurt et al [8] in 2017 presented the comparative performances of the SARIMA and ANN. After which he concluded that ANN had better performance.…”

“…Electric load forecasting is additionally beneficial to the electric utility's financial matters. Load consumption is a key and crucial data for power generation offices and merchants, particularly production planning, everyday operations, unit commitment [8], [26]. Load forecasting of electricity consumption is amongst the critical reason for power trading, water treatment and so on [9], [21], [22].…”

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