Electricity Load Forecasting by Combining Adaptive Neuro-fuzzy Inference System and Seasonal Auto-Regressive Integrated Moving Average
Authors
Abstract
Nowadays, electricity load forecasting, as one of the most important areas, plays a crucial role in the economic process. What separates electricity from other commodities is the impossibility of storing it on a large scale and cost-effective construction of new power generation and distribution plants. Also, the existence of seasonality, nonlinear complexity, and ambiguity pattern in electricity data set makes it more difficult to forecast by using the traditional methods. Therefore, new models, computational intelligence and soft computing tools and combining models are the most accurate and widely used methods for modeling the complexity and uncertainty in the data set. In this paper, a parallel optimal hybrid model using computational intelligence tools and soft computations is proposed to forecast the electricity load forecasting. The main idea of this model is the use of the advantages of the individual models in the modeling of complex systems in a structure and elimination of the limitations of them, simultaneously. The experimental results indicate that the proposed hybrid model has a higher performance accuracy in comparison to iterative suboptimal hybrid models and its computational cost is lower than the other hybrid models; also, the proposed model can achieve more accurate results, as compared with its component and some other seasonal hybrid models.
Keywords
1. Box, G. E. P., and Jenkins, G. M., Time Series Analysis, Forecasting and Control, Holden-Day, San Francisco, 1970.
2. Saab, S., Badr, E., and Nasr, G., “Univariate Modeling and Forecasting of Energy Consumption: the Case of Electricity in Lebanon”, Energy, Vol. 26, No. 1, pp. 1-14, 2001.
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7. Novak, B., “Superfast Autoconfiguring Artificial Neural Networks and Their Application to Power Systems”, Electric Power Systems Research, Vol. 35, pp. 11-16, 1995.
8. Panapakidis, I. P., and Athanasios S. D., “Day-Ahead Electricity Price Forecasting via the Application of Artificial Neural Network Based Models”, Applied Energy, Vol. 172, pp. 132-151, 2016.
9. Zendehboudi, S., Rezaei, N., and Lohi, A., “Applications of Hybrid Models in Chemical, Petroleum, and Energy Systems: A Systematic Review”, Applied Energy, Vol. 228, pp. 2539-2566, 2018.
10. Box, P., and Jenkins, G. M., Time Series Analysis: Forecasting and Control, Holden-day Inc, San Francisco, CA, 1976.
11. Khashei, M., Bijari, M., and Raissi Ardali, Gh. A., “Hybridization of Autoregressive Integrated Moving Average (ARIMA) with Probabilistic Neural Networks (PNNs)”, Computers & Industrial Engineering, Vol. 63, No. 1, pp. 37-45, 2012.
12. Camara A., Feixing, W., and Xiuqin, L., “Energy Consumption Forecasting using Seasonal ARIMA with Artificial Neural Networks Models”, International Journal of Business and Management, Vol. 11, No. 5, p. 231, 2016.
13. Norizan, M., Maizah, A., and Zuhaimy I., “Short Term Load Forecasting using Double Seasonal ARIMA Model, In Proceedings of the Regional Conference on Statistical Sciences, Vol. 10, pp. 57-73, 2010.
14. Soares, L. J, and Medeiros M. C., “Modeling and Forecasting Short-Term Electricity Load: A Comparison of Methods with an Application to Brazilian Data”, International Journal of Forecasting, No. 24, No. 4, pp. 630-44, 2008.
15. Yaslan, Y., and Bahadır B., “Empirical Mode Decomposition Based Denoising Method with Support Vector Regression for Time Series Prediction: A Case Study for Electricity Load Forecasting”, Measurement, Vol. 103, pp. 52-61, 2017
16. Hu, R., Shiping, W., Zhigang, Z., and Tingwen, H., “A Short-Term Power Load Forecasting Model Based on The Generalized Regression Neural Network with Decreasing Step Fruit Fly Optimization Algorithm”, Neurocomputing, Vol. 221, pp. 24-31, 2017.
17. Olagoke, M. D., Ayeni, A., and Hambali, M. A., “Short Term Electric Load Forecasting using Neural Network and Genetic Algorithm”, International Journal of Applied Information Systems, No. 10, pp. 22-28, 2016.
18. Nie, H., Liu, G., Liu, X., and Wang, Y., “Hybrid of ARIMA and SVMs for Short-Term Load Forecasting”, Energy Procedia, Vol. 16, pp. 1455-1460, 2012.
2. Saab, S., Badr, E., and Nasr, G., “Univariate Modeling and Forecasting of Energy Consumption: the Case of Electricity in Lebanon”, Energy, Vol. 26, No. 1, pp. 1-14, 2001.
3. Park, J. H., Park, Y. M., and Lee, K. Y., “Composite Modeling for Adaptive Short-Term Load Forecasting”, IEEE Transactions on Power Systems, Vol. 6, No. 2, pp. 450-457, 1991.
4. Bunn, D. W., and Farmer, E. D., Comparative Models for Electrical Load Forecasting, John Wiley & Sons, New York, 1985.
5. Taylor, J. W., and Buizza, R., “Using Weather Ensemble Predictions in Electricity Demand Forecasting”, International Journal of Forecasting, Vol. 19, pp. 57-70, 2003.
6. Hsu, C. C., and Chen, C. Y., “Regional Load Forecasting in Taiwan: Applications of Artificial Neural Networks”, Energy Conversion and Management, Vol. 44, pp. 1941-1949, 2003.
7. Novak, B., “Superfast Autoconfiguring Artificial Neural Networks and Their Application to Power Systems”, Electric Power Systems Research, Vol. 35, pp. 11-16, 1995.
8. Panapakidis, I. P., and Athanasios S. D., “Day-Ahead Electricity Price Forecasting via the Application of Artificial Neural Network Based Models”, Applied Energy, Vol. 172, pp. 132-151, 2016.
9. Zendehboudi, S., Rezaei, N., and Lohi, A., “Applications of Hybrid Models in Chemical, Petroleum, and Energy Systems: A Systematic Review”, Applied Energy, Vol. 228, pp. 2539-2566, 2018.
10. Box, P., and Jenkins, G. M., Time Series Analysis: Forecasting and Control, Holden-day Inc, San Francisco, CA, 1976.
11. Khashei, M., Bijari, M., and Raissi Ardali, Gh. A., “Hybridization of Autoregressive Integrated Moving Average (ARIMA) with Probabilistic Neural Networks (PNNs)”, Computers & Industrial Engineering, Vol. 63, No. 1, pp. 37-45, 2012.
12. Camara A., Feixing, W., and Xiuqin, L., “Energy Consumption Forecasting using Seasonal ARIMA with Artificial Neural Networks Models”, International Journal of Business and Management, Vol. 11, No. 5, p. 231, 2016.
13. Norizan, M., Maizah, A., and Zuhaimy I., “Short Term Load Forecasting using Double Seasonal ARIMA Model, In Proceedings of the Regional Conference on Statistical Sciences, Vol. 10, pp. 57-73, 2010.
14. Soares, L. J, and Medeiros M. C., “Modeling and Forecasting Short-Term Electricity Load: A Comparison of Methods with an Application to Brazilian Data”, International Journal of Forecasting, No. 24, No. 4, pp. 630-44, 2008.
15. Yaslan, Y., and Bahadır B., “Empirical Mode Decomposition Based Denoising Method with Support Vector Regression for Time Series Prediction: A Case Study for Electricity Load Forecasting”, Measurement, Vol. 103, pp. 52-61, 2017
16. Hu, R., Shiping, W., Zhigang, Z., and Tingwen, H., “A Short-Term Power Load Forecasting Model Based on The Generalized Regression Neural Network with Decreasing Step Fruit Fly Optimization Algorithm”, Neurocomputing, Vol. 221, pp. 24-31, 2017.
17. Olagoke, M. D., Ayeni, A., and Hambali, M. A., “Short Term Electric Load Forecasting using Neural Network and Genetic Algorithm”, International Journal of Applied Information Systems, No. 10, pp. 22-28, 2016.
18. Nie, H., Liu, G., Liu, X., and Wang, Y., “Hybrid of ARIMA and SVMs for Short-Term Load Forecasting”, Energy Procedia, Vol. 16, pp. 1455-1460, 2012.
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