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Energy Consumption Forecasting in Hong Kong Using ARIMA and Artificial Neural Networks Models
Abstract:
There have been considerable efforts contributed to the development of effective energy demand forecast models due to its critical role for economic development and environmental protection. This study focused on the adoption of artificial neural network (ANN) and autoregressive integrated moving average (ARIMA) models for energy consumption forecasting in Hong Kong over the period of 1975-2010. Four predictors were considered, including population, GDP, exports, and total visitor arrivals. The results show most ANN models demonstrate acceptable forecast accuracy when single predictor is considered. The best single input model is the case with GDP as predictor. Population and exports are the next proper single inputs. The model with total visitor arrivals as sole predictor does not perform satisfactorily. This indicates that tourism development demonstrates a different pattern from that of energy consumption. In addition, the forecast accuracy of ANN does not improve considerably as the number of predictors increase. Findings imply that with the ANN approach, choosing appropriate predictors is more important than increasing the number of predictors. On the other hand, ARIMA generates forecasts as accurate as some good cases by ANN. Results suggest that ARIMA is not only a parsimonious but effective approach for energy consumption forecasting in Hong Kong.
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2085-2097
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October 2014
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[1] Hong Kong Tourism Board. Yearly Visitor Arrivals & Spending Hit New Heights. English Language Press Releases, Hong Kong Tourism Board (2011).
[2] British Petroleum. BP Statistical Review of World Energy. June (2013).
[3] Census and Statistics Department, Hong Kong Special Administrative Region. Hong Kong Energy Statistics 2011 Annual Report. Hong Kong, People's Republic of China (2012).
[4] L. Suganthi, A.A. Samuel: Energy models for demand forecasting–A review. Renewable and Sustainable Energy Reviews Vol. 16(2) (2012), pp.1223-1240.
[5] Z. Tao, Y.C. Wong: Hong Kong: from an industrial city to a centre of manufacturing-related services. Urban Studies Vol. 39 (12) (2002), pp.2345-2358.
[6] M. Kankal, A. Akpinar, M. Kömürcü, T.S. Özsahin: Modeling and forecasting of Turkey's energy consumption using socio-economic and demographic variables. Applied Energy Vol. 88 (2011), p.1927-(1939).
[7] P. Zou, J. Yang, J. Fu, G. Liu, and D. Li: Artificial neural network and time series models for predicting soil salt and water content. Agricultural Water Management Vol. 97(12) (2010), p.2009–(2019).
[8] M. Xu, T.C. Wong, and K.S. Chin: Modeling daily patient arrivals at Emergency Department and quantifying the relative importance of contributing variables using artificial neural network, Decision Support Systems Vol. 54(3) (2013), p.1488–1498.
[9] V.Ş. Ediger, and S. Akar: ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy Vol. 35 (2007), pp.1701-1708.
[10] F.K. Chuang, C.Y. Hung, C.Y. Chang and Kuo-Cheng Kuo: Deploying arima and artificial neural networks models to predict energy consumption in Taiwan, Sensor Letters Vol. 11 (2013), p.2333–2340.
DOI: 10.1166/sl.2013.3087
[11] L.A. Díaz-Robles, J.C. Ortega, J.S. Fu, G.D. Reed, J.C. Chow and J.G. Watson, J.A. and Moncada-Herrera: A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: the case of Temuco, Chile. Atmospheric Environment 2008, Vol. 42 (2008).
[12] D.O. Faruk: A hybrid neural network and ARIMA model for water quality time series prediction. Engineering Applications of Artificial Intelligence Vol. 23 (2010), pp.586-594.
[13] H.W. Kim, J.H. Lee, and Y.H. Choi: Dynamic bandwidth provisioning using ARIMA-based traffic forecasting for mobile WiMAX. Computer Communications Vol. 34 (2011), pp.99-106.
[14] V.R. Prybotok, J.S. Yi and D. Mitchell: Comparison of neural network models with ARIMA and regression models for prediction of Houston's daily maximum ozone concentrations European Journal of Operational Research Vol. 122 (2000), pp.31-40.
[15] J-C. Gutierrez-Estrada, Z,E. De Pedro-San, R. López-Luque and I. Pulido-Calvo: Comparison between traditional methods and artificial neural networks for ammonia concentration forecasting in an eel (Anguilla anguilla L. ) intensive rearing system. Aquacultural Engineering Vol. 31(3-4) (2004).
[16] E. Cadenas and W. Rivera: Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANNs model. Renewable Energy Vol. 35 (2010), pp.2732-2738.
[17] A.P. Ansuj, M.E. Camargo, R. Radharamanan, and D.G. Petry: Sales forecasting using time series and neural networks. Computers and Industrial Engineering Vol. 31 (1996), pp.421-424.
[18] K. Kandananond: Forecasting electricity demand in Thailand with an artificial neural network approach Energies Vol. 4(8), (2011) pp.1246-1257.
DOI: 10.3390/en4081246
[19] D. Suh and S. Chang: An energy and water resource demand estimation model for multi-family housing complexes in Korea. Energies Vol. 5(11) (2012), pp.4497-451.
DOI: 10.3390/en5114497
[20] B. Zhu: A novel multiscale ensemble carbon price prediction model integrating empirical mode decomposition, genetic algorithm and artificial neural network. Energies Vol. 5(2) (2012), pp.355-370.
DOI: 10.3390/en5020355
[21] V. Bianco, O. Manca and S. Nardini: Electricity consumption forecasting in Italy using linear regression models. Energy Vol. 34(9) (2009), p.1413–1421.
[22] Information Services Department, Hong Kong Special Administrative Region Government, Hong Kong. The facts: Hong Kong as a Service Economy (2012).
[23] Hong Kong Tourism Industry. Economic Focus 2011 (2011).
[24] A. Kraft, and J. Kraft: On the relationship between energy and GNP. Journal of Energy Development Vol. 3, (1978), pp.401-403.
[25] A. Acaravci and I. Ozturk: On the relationship between energy consumption, CO2 emissions and economic growth in Europe Energy Vol. 35(12) (2010), pp.5412-5420.
[26] K.C. Kuo, C.Y. Chang, M.H. Chen and W.Y. Chen: In search of causal relationship between FDI, GDP, and energy consumption-evidence from China Advanced Materials Research Vol. 524-527 (2012), pp.3388-3391.
[27] S.L. Lai, K.C. Kuo, P. Kanyasathaporn and M. Liu: The causal relationship between economic growth, energy consumption and CO2 emissions in Hong Kong, Energy Education Science and Technology Part A: Energy Science and Research 2014 Vol. 32(1) (2014).
[28] F. Halicioglu: A dynamic econometric study of income, energy and exports in Turkey. Energy Vol. 36(5) (2011), p.3348–3354.
[29] K.C. Kuo, M. Liu and S.L. Lai: Effect of tourism development on energy consumption, CO2 and economic growth in China. Advanced Materials Research Vol. 524-527 (2012), pp.3380-3383.
[30] C.Y. Ho and K.W. Siu: A dynamic equilibrium of electricity consumption and GDP in Hong Kong: An empirical investigation. Energy Policy Vol. 35 (4) (2007), p.2507–2513.
[31] L. Li, Z. Tan, J. Wang, J. Xu, C. Cai and Y. Hou: Energy conservation and emission reduction policies for the electric power industry in China. Energy Policy Vol. 39(6) (2011), pp.3669-3679.
[32] H.T. Pao and C.M. Tsai: Multivariate Granger causality between CO2 emissions, energy consumption, FDI (foreign direct investment) and GDP (gross domestic product): Evidence from a panel of BRIC (Brazil, Russian Federation, India, and China) countries. Energy Vol. 36 (2011).
[33] H.H. Lean and R. Smyth: On the dynamics of aggregate output, electricity consumption and exports in Malaysia: Evidence from multivariate Granger causality tests. Applied Energy Vol. 87 (2010), p.1963-(1971).
[34] C.C. Lee and C.P. Chang: The impact of energy consumption on economic growth: Evidence from linear and nonlinear models in Taiwan. Energy Vol. 32 (2007), pp.2282-2294.
[35] X.P. Zhang and X.M. Cheng: Energy consumption, carbon emissions, and economic growth in China. Ecological Economics Vol. 68 (2009), pp.2706-2712.
[36] Z.W. Geem and W.E. Roper: Energy demand estimation of South Korea using artificial neural network. Energy Policy Vol. 37 (2009), pp.4049-4054.
[37] K. Kavaklioglu, H. Ceylan, H.K. Ozturk and O.E. Canyurt: Modeling and prediction of Turkey's electricity consumption using artificial neural networks. Energy Convers Manage Vol. 50 (2009), pp.2719-2727.
[38] S. Becken and D.G. Simmons: Understanding energy consumption patterns of tourist attractions and activities in New Zealand. Tourism Management Vol. 23(4) (2002), pp.343-354.
[39] J. Liu, T. Feng and X. Yang: The energy requirements and carbon dioxide emissions of tourism industry of Western China: A case of Chengdu city. Renewable and Sustainable Energy Reviews Vol. 15 (2011), pp.2887-2894.
[40] W.M. To, T.M. Lai and W.L. Chung: Fuel life cycle emissions for electricity consumption in the world's gaming center–Macao SAR, China. Energy Vol. 36 (2011), p.5162–5168.
[41] T.M. Lai, W.M. To, W.C. Lo and Y.S. Choy: Modeling of electricity consumption in the Asian gaming and tourism center: Macao SAR, People's Republic of China. Energy Vol. 33 (2008), pp.678-688.
[42] T.M. Lai, W.M. To, W.C. Lo, Y.S. Choy and K.H. Lam: The causal relationship between electricity consumption and economic growth in a Gaming and Tourism Center: The case of Macao SAR, the People's Republic of China. Energy Vol. 36 (2011).
[43] A. Azadeh, S.F. Ghaderi and S. Sohrabkhani: A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran. Energy Policy Vol. 36(7) (2008), pp.2637-2644.
[44] H.T. Pao: Comparing linear and nonlinear forecasts for Taiwan's electricity consumption. Energy Vol. 31 (2006), pp.2129-2142.
[45] A. Sözen and E. Arcaklioglu: Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey. Energy Policy Vol. 35 (2007), pp.4981-4992.
[46] G.A. Darbellay and M. Slama: Forecasting the short-term demand for electricity: do neural networks stand a better chance. International Journal of Forecasting Vol. 16 (2000), pp.71-83.
[47] K. Hornik, M. Stinnchcombe and H. White: Multi-layer feed forward networks are universal approximators. Neural Networks Vol. 2(5) (1989), p.359–366.
[48] M. Khashei and M.A. Bijari: Novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Applied Soft Computing Vol. 11 (2011), pp.2664-2675.
[49] U. Yolcu, E. Egrioglu and C.H. Aladag: A new linear & nonlinear artificial neural network model for time series forecasting, Decision Support Systems Vol. 54(3) (2013), p.1340–1347.
[50] C.J. Lu, T.S. Lee and C.M. Lian: Sales forecasting for computer wholesalers: A comparison of multivariate adaptive regression splines and artificial neural networks, Decision Support Systems, Vol. 54(1) (2012), p.584–596.
[51] M. Khashei, M. Bijari and G.A.R. Ardali: Improvement of auto-regressive integrated moving average models using fuzzy logic and artificial neural networks (ANNs). Neurocomputing Vol. 72(4-6) (2009), pp.956-967.
[52] M. Kutner, C. Nachtsheim, J. Neter and W. Li: Applied Linear Statistical Models, 5th ed. McGraw-Hill: Irwin (2005).
[53] G.E.P. Box, G.W. Jenkins and G. Reinsel: Time Series Analysis, Forecasting, and Control, 3rd ed. Prentice Hall: Englewood Cliffs, N.J. (1994).
[54] G.P. Zhang: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing Vol. 50 (2003), p.159–175.
[55] S.A. DeLurgio: Forecasting Principles and Applications. McGraw-Hill: Irwin (1998).
[56] J.H. Wilson and B. Keating: Business Forecasting with Forecast XTM, 6th ed. John Galt Solutions, Inc., McGraw-Hill: Irwin (2009).
[57] F.X. Diebold: Elements of Forecasting, 4th ed. Thomson: South-western (2007).
[58] M. Belloumi: Energy consumption and GDP in Tunisia: cointegration and causality analysis. Energy Policy Vol. 37(7) (2009), pp.2745-2753.
[59] N. Bowden and J.E. Payne: The causal relationship between US energy consumption and real output: a disaggregated analysis. Journal of Policy Modeling Vol. 31(2) (2009), pp.180-188.
[60] G. Erdal, H. Erdal and K. Esengün: The causality between energy consumption and economic growth in Turkey. Energy Policy Vol. 36(10) (2008), pp.3838-3842.
[61] M. Liu, S.L. Lai and K.C. Kuo: Economic growth, energy consumption and tourism development in Taiwan: A granger causality approach, Advanced Materials Research Vol. 524-527 (2012), pp.3376-3379.
[62] D. Hwang and B. Gum: The causal relationship between energy and GNP: the case of Taiwan. Journal of Energy Development Vol. 16 (1991), pp.219-226.
[63] Y. Wang, J. Zhou, X. Zhu and G. Lu: Energy consumption and economic growth in China: A multivariate causality test. Energy Policy Vol. 39(7) (2011), pp.4399-4406.
[64] A. Kaya and M. Yalcintas: Energy consumption trends in Hawaii. Energy Vol. 35 (2012), pp.1363-1367.
[65] G. Escrivá-Escrivá, C. Álvarez-Bel, C. Roldán-Blay and M. Alcázar-Ortega: New artificial neural network prediction method for electrical consumption forecasting based on building end-use. Energy and Buildings, Vol. 43(11) (2011), pp.3112-3119.
[66] V. Jothiprakash and R.B. Magar: Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent techniques using lumped and distributed data. Journal of Hydrology Vol. 450-451 (2012), pp.293-307.
[67] E. Pisoni, M. Farina, C. Carnevale and L. Piroddi: Forecasting peak air pollution levels using NARX models. Engineering Applications of Artificial Intelligence Vol. 22 (2009), pp.593-602.
[68] I. Drezga and S. Rahman: Input variable selection for ANNs-based short-term load forecasting. IEEE Transactions on Power Systems Vol. 13(4) (1998), p.1238–1244.
DOI: 10.1109/59.736244
[69] P. Pérez and J. Reyes: Prediction of maximum of 24-h average of PM10 concentrations 30-h in advance in Santiago, Chile. Atmospheric Environment Vol. 36 (2002), p.4555–4561.
[70] S.I.V. Sousa, F.G. Martins, M.C. Pereira and M.C.M. Alvim-Ferraz: Prediction of ozone concentrations in Oporto City with statistical approaches. Chemosphere Vol. 64(7) (2006), pp.1141-1149.
[71] S. Thomas and R.B. Jacko: Model for forecasting expressway fine particulate matter and carbon monoxide concentration: application of regression and neural network models. Journal of the Air & Waste Management Association Vol. 57 (2007).
[72] G. Zhang, B.E. Patuwo and M.Y. Hu: Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting Vol. 14 (1998), pp.35-62.