Environmental Effects on Energy Consumption of Smart Grid Consumers
Environment and surrounding plays a pivotal rule in structuring life-style of the consumers. Living standards intern effect the energy consumption of the consumers. In smart grid paradigm, climate drifts, weather parameter and green environmental directly relates to the energy profiles of the various consumers, such as residential, commercial and industrial. Considering above factors helps policy in shaping utility load curves and optimal management of demand and supply. Thus, there is a pressing need to develop correlation models of load and weather parameters and critical analysis of the factors effecting energy profiles of smart grid consumers. In this paper, we elaborated various environment and weather parameter factors effecting demand of consumers. Moreover, we developed correlation models, such as Pearson, Spearman, and Kendall, an inter-relation between dependent (load) parameter and independent (weather) parameters. Furthermore, we validated our discussion with real-time data of Texas State. The numerical simulations proved the effective relation of climatic drifts with energy consumption of smart grid consumers.
[1] Oliveira, M. O., D. P. Marzec, G. Bordin, A. S. Bretas, and D. Bernardon. "Climate Change Effect on Very Short-term Electric Load Forecasting." 2011 IEEE Trondheim PowerTech (2011): n. pag. Web.
[2] Chen, Gerk Hing, and Tek Tjing Lie. "The Impact of Climate Change on New Zealand's Electricity Demand." 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems (2010): n. pag. Web.
[3] Hor, C.-L., S.j. Watson, and S. Majithia. "Analyzing the Impact of Weather Variables on Monthly Electricity Demand." IEEE Trans. Power Syst. IEEE Transactions on Power Systems 20.4 (2005): 2078-085. Web.
[4] Panteli, M., Mancarella, P., Cotton, I., Pickering, C., Wilkinson, S., Anderson, K., … Dawson, R. (2015). Impact of climate change on the resilience of the UK power system. IET International Conference on Resilience of Transmission and Distribution Networks (RTDN) 2015. doi:10.1049/cp.2015.0878
[5] Liu, K., Wang, M., Tian, Z., & Xiang, C. (2012). The Research about the Impact of Climate Change on Power Load of Air Conditioning. 2012 Asia-Pacific Power and Energy Engineering Conference. doi:10.1109/appeec.2012.6307560
[6] Douglas, A., Breipohl, A., Lee, F., & Adapa, R. (1998). The impacts of temperature forecast uncertainty on Bayesian load forecasting. IEEE Trans. Power Syst. IEEE Transactions on Power Systems, 13(4), 1507-1513. doi:10.1109/59.736298
[7] Parkpoom, S.j., and G.p. Harrison. "Analyzing the Impact of Climate Change on Future Electricity Demand in Thailand." IEEE Trans. Power Syst. IEEE Transactions on Power Systems 23.3 (2008): 1441-448. Web
[8] Paul, Jai Sachith, Akhil P. Sivan, and K. Balachandran. "Energy Sector in India: Challenges and Solutions." 2013 International Conference on Green Computing, Communication and Conservation of Energy (ICGCE) (2013): n. pag. Web.
[9] Liu, Fei. "Application of Environmental Management on Energy Saving and Green House Gas Reduction in Beijing." 2010 International Conference on Management and Service Science (2010): n. pag. Web.
[10] Azadeh, A., Saberi, M., & Seraj, O. (2010). An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran. Energy, 35(6), 2351-2366. doi: 10.1016/j.energy.2009.12.023
[11] D. Paravan, A. Debs, C. Hansen, D. Becker, P. Hirsch, and R. Golob.“Influence of temperature on short-term load forecasting using the EPRI-ANNSTLF.”
[12] A. Pardo, V. Meneu, and E. Valor, “Temperature and seasonality influences on Spanish electricity load,” Energy Economics, vol. 24, pp. 55-70, 2002.
[13] Lal, M.; Harasawa H.; Murdiyarso, D. Asia. In: Climate Change 2001 Impacts, Adaptation and Vulnerability. McCarthy, J.J.; Canziani. O.F.; Leary, N.A.; Dokken D.J.; and White K.S. (Eds.). Cambridge University Press, Chap+ I 1 I 2001
[14] S. S. Wyer, “A treatise on producer-gas and gas-producers,” The Engineering and Mining Journal, London, pp. 23, 1906.
[15] Fahad, Muhammad Usman, and Naeem Arbab. "Factor Affecting Short Term Load Forecasting." Journal of Clean Energy Technologies JOCET 2.4 (2014): 305-09. Web
[16] L. Hadjioannou, “Three years of operation of the radiation centre in Nicosia”, Cyprus. Meteorological Note Series No. 2, Meteorological Service, March (1987).
[17] S. M. Ali, C. A. Mehmood, M. Jawad, B. Khan, U. Farid, A. Majid, M. Ali, M. Jawad, “Stochastic and Statistical Analysis of Utility Revenues and Weather Data Analysis for Consumer Demand Estimation in Smart Grid”, PLOS ONE J., 2016
[18] S. M. Ali, M. Asif, “Energy Management Scheme for Residential Community in Smart Grids using FEDRP”, Canadian J. of Electrical and Electronic Engineering, vol. 3, no. 8, pp. 439-447, 2012.
[19] "U.S. Energy Information Administration - EIA - Independent Statistics and Analysis." U.S. Energy Information Administration (EIA). N.p., n.d. Web. 14 July 2016.
[1] Oliveira, M. O., D. P. Marzec, G. Bordin, A. S. Bretas, and D. Bernardon. "Climate Change Effect on Very Short-term Electric Load Forecasting." 2011 IEEE Trondheim PowerTech (2011): n. pag. Web.
[2] Chen, Gerk Hing, and Tek Tjing Lie. "The Impact of Climate Change on New Zealand's Electricity Demand." 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems (2010): n. pag. Web.
[3] Hor, C.-L., S.j. Watson, and S. Majithia. "Analyzing the Impact of Weather Variables on Monthly Electricity Demand." IEEE Trans. Power Syst. IEEE Transactions on Power Systems 20.4 (2005): 2078-085. Web.
[4] Panteli, M., Mancarella, P., Cotton, I., Pickering, C., Wilkinson, S., Anderson, K., … Dawson, R. (2015). Impact of climate change on the resilience of the UK power system. IET International Conference on Resilience of Transmission and Distribution Networks (RTDN) 2015. doi:10.1049/cp.2015.0878
[5] Liu, K., Wang, M., Tian, Z., & Xiang, C. (2012). The Research about the Impact of Climate Change on Power Load of Air Conditioning. 2012 Asia-Pacific Power and Energy Engineering Conference. doi:10.1109/appeec.2012.6307560
[6] Douglas, A., Breipohl, A., Lee, F., & Adapa, R. (1998). The impacts of temperature forecast uncertainty on Bayesian load forecasting. IEEE Trans. Power Syst. IEEE Transactions on Power Systems, 13(4), 1507-1513. doi:10.1109/59.736298
[7] Parkpoom, S.j., and G.p. Harrison. "Analyzing the Impact of Climate Change on Future Electricity Demand in Thailand." IEEE Trans. Power Syst. IEEE Transactions on Power Systems 23.3 (2008): 1441-448. Web
[8] Paul, Jai Sachith, Akhil P. Sivan, and K. Balachandran. "Energy Sector in India: Challenges and Solutions." 2013 International Conference on Green Computing, Communication and Conservation of Energy (ICGCE) (2013): n. pag. Web.
[9] Liu, Fei. "Application of Environmental Management on Energy Saving and Green House Gas Reduction in Beijing." 2010 International Conference on Management and Service Science (2010): n. pag. Web.
[10] Azadeh, A., Saberi, M., & Seraj, O. (2010). An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran. Energy, 35(6), 2351-2366. doi: 10.1016/j.energy.2009.12.023
[11] D. Paravan, A. Debs, C. Hansen, D. Becker, P. Hirsch, and R. Golob.“Influence of temperature on short-term load forecasting using the EPRI-ANNSTLF.”
[12] A. Pardo, V. Meneu, and E. Valor, “Temperature and seasonality influences on Spanish electricity load,” Energy Economics, vol. 24, pp. 55-70, 2002.
[13] Lal, M.; Harasawa H.; Murdiyarso, D. Asia. In: Climate Change 2001 Impacts, Adaptation and Vulnerability. McCarthy, J.J.; Canziani. O.F.; Leary, N.A.; Dokken D.J.; and White K.S. (Eds.). Cambridge University Press, Chap+ I 1 I 2001
[14] S. S. Wyer, “A treatise on producer-gas and gas-producers,” The Engineering and Mining Journal, London, pp. 23, 1906.
[15] Fahad, Muhammad Usman, and Naeem Arbab. "Factor Affecting Short Term Load Forecasting." Journal of Clean Energy Technologies JOCET 2.4 (2014): 305-09. Web
[16] L. Hadjioannou, “Three years of operation of the radiation centre in Nicosia”, Cyprus. Meteorological Note Series No. 2, Meteorological Service, March (1987).
[17] S. M. Ali, C. A. Mehmood, M. Jawad, B. Khan, U. Farid, A. Majid, M. Ali, M. Jawad, “Stochastic and Statistical Analysis of Utility Revenues and Weather Data Analysis for Consumer Demand Estimation in Smart Grid”, PLOS ONE J., 2016
[18] S. M. Ali, M. Asif, “Energy Management Scheme for Residential Community in Smart Grids using FEDRP”, Canadian J. of Electrical and Electronic Engineering, vol. 3, no. 8, pp. 439-447, 2012.
[19] "U.S. Energy Information Administration - EIA - Independent Statistics and Analysis." U.S. Energy Information Administration (EIA). N.p., n.d. Web. 14 July 2016.
@article{"International Journal of Electrical, Electronic and Communication Sciences:74552", author = "S. M. Ali and A. Salam Khan and A. U. Khan and M. Tariq and M. S. Hussain and B. A. Abbasi and I. Hussain and U. Farid", title = "Environmental Effects on Energy Consumption of Smart Grid Consumers", abstract = "Environment and surrounding plays a pivotal rule in structuring life-style of the consumers. Living standards intern effect the energy consumption of the consumers. In smart grid paradigm, climate drifts, weather parameter and green environmental directly relates to the energy profiles of the various consumers, such as residential, commercial and industrial. Considering above factors helps policy in shaping utility load curves and optimal management of demand and supply. Thus, there is a pressing need to develop correlation models of load and weather parameters and critical analysis of the factors effecting energy profiles of smart grid consumers. In this paper, we elaborated various environment and weather parameter factors effecting demand of consumers. Moreover, we developed correlation models, such as Pearson, Spearman, and Kendall, an inter-relation between dependent (load) parameter and independent (weather) parameters. Furthermore, we validated our discussion with real-time data of Texas State. The numerical simulations proved the effective relation of climatic drifts with energy consumption of smart grid consumers.
", keywords = "Climatic drifts, correlation analysis, energy consumption, smart grid, weather parameter.", volume = "11", number = "1", pages = "27-10", }
