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. 2017 Apr 20;12(4):e0175915.
doi: 10.1371/journal.pone.0175915. eCollection 2017.

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

Ömer Özgür Bozkurt  1 , Göksel Biricik  1 , Ziya Cihan Tayşi  1
Affiliations
Free PMC article

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

Ömer Özgür Bozkurt et al. PLoS One. .
Free PMC article

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.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Feed-forward neural network design.
Fig 2
Fig 2. Comparison of feature importances on load.
Fig 3
Fig 3. Power load in four consecutive weeks of March 2014.
Fig 4
Fig 4. Autocorrelation function and partial autocorrelation function of load.
Fig 5
Fig 5. Load estimation of both SARIMA models for the last week of January 2014.
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.
Fig 6
Fig 6. Load estimation of both SARIMA models for the last week of July 2014.
Estimations for week 7, BIC based SARIMA model is shown in the upper part. Estimations of intuitive SARIMA model are given in the lower part.
Fig 7
Fig 7. Performance comparison of NN learning methods across feature sets, 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.
Fig 8
Fig 8. Performance comparison of the proposed approaches, measured with APE.
MAPE values are highlighted on min-max intervals. Smaller values mean higher forecast accuracy.
Fig 9
Fig 9. Load estimations of SARIMA based model and actual load values for 12 weeks of year 2014.
Fig 10
Fig 10. Load estimations of ANN based model and actual load values for 12 weeks of year 2014.
Fig 11
Fig 11. Empirical cumulative distribution functions for MAPEs of SARIMA and ANN based models on 12 test weeks of year 2014.
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Grants and funding

The research stands on the project funded by the Scientific and Technological Research Council Of Turkey, Research, Development & Innovation Grant Programme (TÜBİTAK SME-RDI) with number 7140008 and the previous studies of the authors. OOB: The Scientific and Technological Research Council of Turkey, Technology and Innovation Funding Programmes Directorate, Information Technologies Grant Committee. Grant Number: 7140008. URL: https://www.tubitak.gov.tr/en/funds/industry/national-support-programmes. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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