A Hybrid Modeling of Long-Term Electricity Consumption Forecasting Based on ARIMA and ANN: The Case of Thailand with Projection

  • Authors

    • Nattapon Jaisumroum
    • Jirarat Teeravaraprug
    2018-05-16
    https://doi.org/10.14419/ijet.v7i2.28.12875
  • ARIMA, Hybrid Forecasting Model, Neural Network, Thailand Electricity Consumption

  • Modeling and forecasting of electricity consumption can provide reliable guidance for power operation and planning in developing countries such as Thailand. In this study, formulates the effects of two different historical data type is modeled by auto regressive integrated moving averaged (ARIMA) and artificial neural network (ANN) based on population and gross domestic product per capita (GDP). The derived model is validated by various statistical approaches such as the determination coefficient. Additionally, the performances of the derived model are assessed using mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE). Three scenarios are used for forecasting Thailand’s electricity consumption in 2011 – 2015.  The simulation results are validated by actual data sets observed from 1993 to 2010. Empirical results showed that the proposed method has higher accuracy compared to single ARIMA and artificial intelligence based models.

     

     

  • References

    1. [1] Wang, Xifan, and James Rufus McDonald. Modern power system planning. McGraw-Hill Companies, 1994.

      [2] Lee, Yi-Shian, and Lee-Ing Tong. "Forecasting energy consumption using a grey model improved by incorporating genetic programming." Energy Conversion and Management 52.1 (2011): 147-152.

      [3] Ekonomou, L. "Greek long-term energy consumption prediction using artificial neural networks." Energy 35.2 (2010): 512-517.

      [4] Suganthi, L., and Anand A. Samuel. "Energy models for demand forecasting—A review." Renewable and sustainable energy reviews 16.2 (2012): 1223-1240.

      [5] Zhang, G. Peter. "Time series forecasting using a hybrid ARIMA and neural network model." Neurocomputing 50 (2003): 159-175.

      [6] Sözen, Adnan, Erol Arcaklioğlu, and Mehmet Özkaymak. "Turkey’s net energy consumption." Applied Energy 81.2 (2005): 209-221.

      [7] Ekonomou, L. "Greek long-term energy consumption prediction using artificial neural networks." Energy 35.2 (2010): 512-517.

      [8] Kankal, Murat, et al. "Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables." Applied Energy 88.5 (2011): 1927-1939.

      [9] Kialashaki, Arash, and John R. Reisel. "Development and validation of artificial neural network models of the energy demand in the industrial sector of the United States." Energy 76 (2014): 749-760.

      [10] Roy, Sumit, et al. "Development and validation of a GEP model to predict the performance and exhaust emission parameters of a CRDI assisted single cylinder diesel engine coupled with EGR." Applied Energy 140 (2015): 52-64.

      [11] Zhang, G. Peter. "Time series forecasting using a hybrid ARIMA and neural network model." Neurocomputing 50 (2003): 159-175.

      [12] Suganthi, L., S. Iniyan, and Anand A. Samuel. "Applications of fuzzy logic in renewable energy systems–a review." Renewable and Sustainable Energy Reviews 48 (2015): 585-607.

      [13] Nadimi, Vahid, et al. "An Adaptive-Network-Based Fuzzy Inference System for Long-Term Electric Consumption Forecasting (2008-2015): A Case Study of the Group of Seven (G7) Industrialized Nations: USA, Canada, Germany, United Kingdom, Japan, France and Italy." Computer Modeling and Simulation (EMS), 2010 Fourth UKSim European Symposium on. IEEE, 2010.

      [14] Hong, Wei-Chiang. "Electric load forecasting by support vector model." Applied Mathematical Modelling 33.5 (2009): 2444-2454.

      [15] Wang, Jianjun, et al. "An annual load forecasting model based on support vector regression with differential evolution algorithm." Applied Energy 94 (2012): 65-70.

      [16] Ardakani, F. J., and M. M. Ardehali. "Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types." Energy 65 (2014): 452-461.

      [17] Nadimi, Vahid, et al. "An Adaptive-Network-Based Fuzzy Inference System for Long-Term Electric Consumption Forecasting (2008-2015): A Case Study of the Group of Seven (G7) Industrialized Nations: USA, Canada, Germany, United Kingdom, Japan, France and Italy." Computer Modeling and Simulation (EMS), 2010 Fourth UKSim European Symposium on. IEEE, 2010.

      [18] Azadeh, A., S. F. Ghaderi, and S. Sohrabkhani. "A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran." Energy Policy 36.7 (2008): 2637-2644.

      [19] Azadeh, Ali, et al. "A hybrid simulation-adaptive network based fuzzy inference system for improvement of electricity consumption estimation." Expert Systems with Applications 36.8 (2009): 11108-11117.

      [20] Azadeh, Ali, et al. "A hybrid simulation-adaptive network based fuzzy inference system for improvement of electricity consumption estimation." Expert Systems with Applications 36.8 (2009): 11108-11117.

      [21] Amjadi, M. H., H. Nezamabadi-Pour, and M. M. Farsangi. "Estimation of electricity demand of Iran using two heuristic algorithms." Energy Conversion and Management 51.3 (2010): 493-497.

      [22] Azadeh, Ali, et al. "Improved estimation of electricity demand function by integration of fuzzy system and data mining approach." Energy Conversion and Management 49.8 (2008): 2165-2177.

      [23] Li, Hong-Ze, et al. "A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm." Knowledge-Based Systems 37 (2013): 378-387.

      [24] Azadeh, Ali, et al. "A hybrid simulation-adaptive network based fuzzy inference system for improvement of electricity consumption estimation." Expert Systems with Applications 36.8 (2009): 11108-11117.

      [25] Li, Hongze, et al. "Annual electric load forecasting by a least squares support vector machine with a fruit fly optimization algorithm." Energies 5.11 (2012): 4430-4445.

      [26] Hamzacebi, Coskun, and Huseyin Avni Es. "Forecasting the annual electricity consumption of Turkey using an optimized grey model." Energy 70 (2014): 165-171.

      [27] Wang, Yuanyuan, et al. "Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China." Energy Policy 48 (2012): 284-294.

      [28] Liu, Xiaojuan, and Jian’an Fang. "Long-term load forecasting based on a time-variant ratio multiobjective optimization fuzzy time series model." Mathematical Problems in Engineering 2013 (2013).

      [29] Mostafavi, Elham Sadat, et al. "A novel machine learning approach for estimation of electricity demand: An empirical evidence from Thailand." Energy conversion and management 74 (2013): 548-555.

      [30] Ekonomou, L. "Greek long-term energy consumption prediction using artificial neural networks." Energy 35.2 (2010): 512-517.

      [31] Padmakumari, K., K. P. Mohandas, and S. Thiruvengadam. "Long term distribution demand forecasting using neuro fuzzy computations." International Journal of Electrical Power & Energy Systems 21.5 (1999): 315-322.

      [32] Ardakani, F. J., and M. M. Ardehali. "Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types." Energy 65 (2014): 452-461.

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  • How to Cite

    Jaisumroum, N., & Teeravaraprug, J. (2018). A Hybrid Modeling of Long-Term Electricity Consumption Forecasting Based on ARIMA and ANN: The Case of Thailand with Projection. International Journal of Engineering & Technology, 7(2.28), 20-23. https://doi.org/10.14419/ijet.v7i2.28.12875