Modeling short-run electricity demand with long-term growth rates and consumer price elasticity in commercial and industrial sectors

Pielow, Amy; Sioshansi, Ramteen; Roberts, Matthew C.

doi:10.1016/j.energy.2012.07.059

Cited by 40 publications

(13 citation statements)

References 13 publications

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“…It does not need to be uncorrelated either serially or with the regressors for consistent estimation, provided that such an equilibrium (cointegrating vector) exists. We acknowledge that by focusing explicitly on the electricity market we are not modeling a complete demand system, as has recently been done by Serletis and Shahmoradi (2008) and Serletis et al (2010a) for 2 While many of the studies on electricity demand focus on residential demand (e.g., Halvorsen, 1975;Maddala et al, 1997;Silk and Joutz, 1997), there have also been studies on the channels of electricity usage in the commercial and industrial sectors (e.g., Berndt and Wood, 1975;Halvorsen, 1978; or more recently Bernstein and Madlener, 2010;Pielow et al, 2012). the US energy market, Serletis et al (2010bSerletis et al ( , 2011 for energy markets across a panel of OECD and non-OECD countries, and Chang and Serletis (2014) for the Canadian transportation sector.…”

Section: Benchmark Modelsmentioning

confidence: 98%

“…Since we estimate long-run elasticities and control for seasonality and higher-frequency cycles in the construction of the data (see Section 3), the purpose of the periodic functions is not to capture such cycles, in contrast to Hinich and Serletis (2006) or Pielow et al (2012). λ 1 = ⋯ = λ p + 2q = 0 for other values of p and q.…”

Section: Time-varying Coefficient Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Time-varying Long-run Income and Output Elasticities of Electricity Demand with an Application to Korea

et al. 2014

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Section: Benchmark Modelsmentioning

confidence: 98%

Section: Time-varying Coefficient Modelsmentioning

confidence: 99%

Time-varying Long-run Income and Output Elasticities of Electricity Demand with an Application to Korea

et al. 2014

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“…Aman and Ping [26] used the MEAD model to forecast the hourly demand of the steel sector in Malaysia , with one year historical data used to define the seasonal, daily and hourly fluctuations in the sector. Pielow et al [27] presented a regression model to generate the hourly electricity demand for the entire year in both the industrial and commercial sector of three cities in US. The model used four years historical hourly datasets and takes into account weather and calander variables.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling and forecasting hourly electricity demand in West African countries

Adeoye

Spataru

2019

Applied Energy

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The Economic Community of West African States aims to achieve 100% electrification rates by 2030 in all member countries. To achieve this ambitious target, electricity generation capacities need to be increased significantly. Forecasting hourly electricity demand is imperative for capacity planners in optimizing investment options and ensuring reliable electricity supply. However, modelling hourly electricity demand in developing countries can be a challenge due to paucity of historical demand data and methodological frameworks that adequately capture technology transitions and urban-rural communities. In this study, we address this gap by developing an hourly electricity demand model for 14 West African countries in the year 2016 and 2030.The model takes into accounts electrification rates, available household appliances, occupancy patterns of household members, type of day, available daylight hours and hourly weather conditions. Annual electricity demand in nonresidential sectors and electricity access rates in urban and rural households are forecasted using multiple regression analysis. We validated the developed model using actual 2016 monthly and annual electricity demand data. The results show the seasonal variations of electricity demand, with hourly electricity demand in dry seasons relatively higher than demand in wet seasons. The results also indicate that in 2030, electricity demand in the West African region is estimated to be five times its 2016 level. The methodology presented in this study can be applicable for modelling hourly electricity demand in developing countries that have scarce historical hourly demand data, a significant electricity supply-demand gap, and varying electricity access rates in urban and rural areas.

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“…Various analysis methodologies have been developed: they have used the regression model [37][38][39][40]; time-series analysis [41][42][43][44][45][46][47][48][49]; and clustering techniques [46][47][48][49][50][51][52][53][54]. However, most analyses have been aimed at short-and medium-term demand forecasting; relatively few analyses have been directed at tailor-made feedback.…”

Section: Analysis Methodology Of Residential Electricity-use Datamentioning

confidence: 99%

Tailor-Made Feedback to Reduce Residential Electricity Consumption: The Effect of Information on Household Lifestyle in Japan

2017

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Abstract:Residential smart metering and energy feedback have attracted worldwide attention toward reducing energy consumption and building a sustainable society. Many theoretical studies have suggested the importance of personalized information; however, few feedback demonstrations have focused on household lifestyle. This paper presents a pilot program of energy feedback reports based on analytical methods to show the relationship between electricity consumption and household lifestyle in Japan. One type of report was for households with a night-oriented lifestyle, which were classified by means of frequency analysis; it was evident that such households should shift to a healthy, environmentally friendly, morning-oriented lifestyle. Another type of report was based on cluster analysis: it pinpointed the dates and times when the household consumed much more electricity than with its regular routine. Through panel data regression analysis, it was found that the reports contributed to reducing daily household electricity consumption-as long as a boomerang effect could be avoided. It was also found that the feedback effect was enhanced by activation of consciousness, norms, and motives. It was observed that activation required a good understanding of the characteristics of electricity consumption and lifestyles of each household.

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Modeling short-run electricity demand with long-term growth rates and consumer price elasticity in commercial and industrial sectors

Cited by 40 publications

References 13 publications

Time-varying Long-run Income and Output Elasticities of Electricity Demand with an Application to Korea

Time-varying Long-run Income and Output Elasticities of Electricity Demand with an Application to Korea

Modelling and forecasting hourly electricity demand in West African countries

Tailor-Made Feedback to Reduce Residential Electricity Consumption: The Effect of Information on Household Lifestyle in Japan

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