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International Journal of Academic Research in Economics and Management Sciences

Open Access Journal

ISSN: 2226-3624

Forecasting the Monthly Electricity Demand of Georgia using Competitive Models and Advices for the Strategic Planning

Ahmet Demira, Salih Ozsoy

http://dx.doi.org/10.6007/IJAREMS/v3-i5/1189

Open access

Electricity is a kind of energy that consumption increases rapidly. However, Electricity is used from the residences to the industry everywhere. This non-storable energy needs to be predicted precisely and supplied sufficiently. For this reason in this research electricity demand of Georgia is predicted using comparative models as Box Jenkins and Neural Network. By this way, an accurate model was developed to perform a strategic plan. For the calculation of significance level, MAPE is utilized to see error. It was seen that artificial neural network multilayer perceptron model provided better predictions rather than SARIMA model. Finally, some suggestions are expressed to the government of Georgia for future plans.

Armstrong, J. S. (1983). Strategic Planning and Forecasting Fundamentals, New York: McGraw Hill.
Aydinalp, M., Ismet Ugursal, V., & Fung, A. S. (2002). Modelling of the appliance, lighting and space-cooling energy consumptions in the residential sector using neural networks. Applied Energy, 71, 87–110.
A. Kheirkhah, A. A., M. Saberi, A. Azaron, H. Shakouri (2012). "Improved estimation of electricity demand function by using of artificial neural network, principal component analysis and data envelopment analysis." Computers & Industrial Engineering 64: 425–441.
CERVONE, H. F. (2014). "Strategic Planning and Assessment." Journal of Library Administration 55: 155-168.
Chandler, A. (1969). Strategy and structure: Chapters in the history of the American industrial enterprise. Cambridge, MA: MIT Press; reprint, 1990.
Chiang, W. C., Urban, T. L., & Baldridge, G. W. (1996). A neural network approach to mutual fund net asset value forecasting. Omega, 24, 205–215.
David, F. R. (2011). Strategic Management: Concepts and Cases. New Jersey, Pearson Education.
Dillon, T. S., Sestito, S., & Leuang, S. (1991). Short term load forecasting using an adaptive neural network. International Journal of Electrical Power & Energy Systems, 13(4), 186–192.
Demir, A. (2014). "ELABORATION OF ELECTRICITY ENERGY FOR PRODUCTION - CONSUMPTION RELATION OF NORTHERN - IRAQ FOR THE FUTURE EXPECTATIONS." International Journal of Acdemic Research in Economics and Management Sciences 3(5): 101-106.
Gareta, R., Romeo, L. M., & Gil, A. (2006). Forecasting of electricity prices with neural networks. Energy Conversion and Management, 47, 1770–1778.
Hsu, C. C., & Chen, C. Y. (2003). Regional load forecasting in Taiwan—Applications of artificial neural networks. Energy Conversion and Management, 44, 1941–1949.
Hill, T., O’Connor, M., & Remus, W. (1996). Neural network models for time series forecasts. Management Science, 42(7), 1082–1092.
Indro, D. C., Jiang, C. X., Patuwo, B. E., & Zhang, G. P. (1999). Predicting mutual fund performance using artificial neural networks. Omega, 27, 373–380.
Jhee, W. C., & Lee, J. K. (1993). Performance of neural networks in managerial forecasting. Intelligent Systems in Accounting, Finance and Management, 2, 55–71.
Kucukdeniz, T. (2010). "Long Term Electricity Demand Forecasting: An Alternative Approach with Support Vector Machines." Istanbul Universitesi Muhendislik Bilimleri Dergisi(1): 45-53.
Kalogirou, S. A. 2000. Applications of artificial neural-networks for energy systems. Appl. Energ. 67:17–35.
Karunasinghe, D. S. K., & Liong, S. Y. (2006). Chaotic time series prediction with a global model artificial neural network. Journal of Hydrology, 323, 92–105.
Kohzadi, N., Boyd, M. S., Kermanshahi, B., & Kaastra, I. (1996). A comparison of artificial neural network and time series models for forecasting commodity prices. Neurocomputing, 10(3), 169–181.
Ming Meng , D. N. a. W. S. (2011). "Forecasting Monthly Electric Energy Consumption Using Feature Extraction." Energies 4: 1495-1507.
Nayak, P. C., Sudheer, K. P., Rangan, D. M., & Ramasastri, K. S. (2004). A neuro-fuzzy computing technique for modeling hydrological time series. Journal of Hydrology, 291, 52–66.
Oliveira, A. L. I., & Meira, S. R. L. (2006). Detecting novelties in time series through neural networks forecasting with robust confidence intervals. Neurocomputing, 70(1–3), 79–92.
Periklis Gogas, A. S. (2008). "Forecasting in Inefficient Commodity Markets." Journal of Economic Studies 36(4): 383-392.
Peng, T. M., Hubele, N. F., & Karady, G. G. (1992). Advancement in the application of neural networks for short term load forecasting. IEEE Transaction on Power System, 7(1), 250–258.
R. Ramakrishna, N. K. B. a. M. K. R. (2012). "Neural Networks forecasting Model for Monthly Electricity Load in Andhra Pradesh." International Journal of Engineering Research and Applications (IJERA) 2(1): 1108-1115.
Raffi Sevlian, R. R. (2014). "Short Term Electricity Load Forecasting on Varying Levels of Aggregation." arXiv:1404.0058 2.
Ringwood, J. V., D. Bofelli, and F.T. Murray (2001). "Forecasting Electricity Demand on Short, Medium and Long Time Scales Using Neural Networks." Journal of Intelligent and Robotic Systems 26(7): 645-657.
Sanjeev Kumar Aggarwal, L. M. S., and Ashwani Kumar (2009). "Short term Price Forecasting in Deregulated Electricity Markets." International Journal of Energy Sector Management 3(4): 333-358.
Sergey Voronin, J. P. (2012). A HYBRID ELECTRICITY PRICE FORECASTING MODEL FOR THE FINNISH ELECTRICITY SPOT MARKET International Symposium on Forecasting 2012, Finland. Steiner, G. A. (1979). Strategic Planning, Free Press.
Stern, H. S. (1996). Neural networks in applied statistics. Technometrics, 38(3), 205–214.
Srinivasa RaoRallapalli, S. (2012). "Forecasting monthlypeakdemandofelectricityinIndia-A critique." Energy Policy 4: 516-520.
V. Özdemir (2014) A Future Projection of Turkey's Energy Intensity, Energy Sources, Part B: Economics, Planning, and Policy, 9:1, 1-8
Yao, S. J., Song, Y. H., Zhang, L. Z., & Cheng, X. Y. (2000). Wavelet transform and neural networks for short-term electrical load forecasting. Energy Conversion and Management, 41, 1975–1988.
Zhang, B. L., and Z. Y. Dong (2001). "An Adaptive Neural-Wavelet Model for Short Term Load Forecasting." Electric Power Systems Research 59(2): 121-129.
(2013). Meeting with Chinese Company Representatives. E. a. S. D. Ministry. http://www.economy.ge/en/media/news/meeting-with-chinese-company-representatives.
(2013). Potential. M. o. E. o. Georgia.
http://www.energy.gov.ge/energy.php?id_pages=60&lang=eng.

(Demir & Ozsoy, 2014)
Demir, A., & Ozsoy, S. (2014). Forecasting the Monthly Electricity Demand of Georgia using Competitive Models and Advices for the Strategic Planning. International Journal of Academic Research in Economics and Management Sciences, 3(5), 78–89.