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

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

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

This is the dataset for the manuscript "Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market" submitted to the journal PLOS ONE

BIC values of SARIMA models with different <i>p</i> & <i>q</i> values.

<p>BIC values of SARIMA models with different <i>p</i> & <i>q</i> values.</p

Load estimation of both SARIMA models for the last week of January 2014.

<p>Estimations for week 1, BIC based SARIMA model is shown in the upper part. Estimations of intuitive SARIMA model are given in the lower part.</p

Load estimations of ANN based model and actual load values for 12 weeks of year 2014.

<p>Load estimations of ANN based model and actual load values for 12 weeks of year 2014.</p

Empirical cumulative distribution functions for MAPEs of SARIMA and ANN based models on 12 test weeks of year 2014.

<p>Empirical cumulative distribution functions for MAPEs of SARIMA and ANN based models on 12 test weeks of year 2014.</p

Performance comparison of NN learning methods across feature sets, measured with MAPE (%).

<p>Smaller MAPE means higher forecast accuracy. D refers to calendar data, L is previous load estimation plan, P is electricity price, W is weather and C is currency feature sets.</p

Comparison of proposed SARIMA models.

<p>Comparison of proposed SARIMA models.</p

Autocorrelation function and partial autocorrelation function of load.

<p>Autocorrelation function and partial autocorrelation function of load.</p

Learning method performance evaluation across different feature sets, grouped by training set length.

<p>Performance is measured with MAPE (%). Smaller MAPE means higher forecast accuracy. D refers to calendar data, L is previous load estimation plan, P is electricity price, W is weather and C is currency feature sets.</p