Non‐Linearities in Electricity Demand and Temperature: Parametric Versus Non‐Parametric Methods

Henley, Andrew; Peirson, John

doi:10.1111/1468-0084.00054

Cited by 97 publications

(38 citation statements)

References 8 publications

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“…Service demand should increase with income and decrease with service price [9,26]. In economic terms we can identify a satiation point for energy service comfort: below the satiation point the marginal utility of an energy service is positive; above it, the marginal utility turns negative [27,28]. In the current study we evaluate income-induced service demand in the future by introducing satiation factors to calibrate our model estimates.…”

Section: Model Calibrationmentioning

confidence: 99%

Energy Service Demand Projections and CO2 Reduction Potentials in Rural Households in 31 Chinese Provinces

et al. 2015

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Until 2012, most of China's population lived in rural areas with markedly different patterns of household energy consumption from those in Chinese cities. The studies so far done on residential energy use in rural Chinese households have been limited to questionnaire surveys and panel data analyses. Hardly any studies on energy demand in rural areas have considered both the climatic and economic disparities across Chinese regions. In this study we conduct a systematic analysis of the rural Chinese residential sector on a regional basis. We begin by developing a macro-model to estimate energy service demands up to 2050. Next, we apply the AIM(Asia-Pasific Integrated Model)/Enduse model, a bottom-up cost-minimization model with a detailed mitigation technology database, to estimate the mitigation potential of low-carbon technologies in rural China. Our results show that energy service demand in the rural household sector will continue to increase in regions with growing population or income conditions. However, after 2030, the rural residential energy service demand will start to decline in most Chinese regions. The impacts of efficient technologies will vary from one region to the next due to regional climatic and economic disparities. Throughout all of China, the penetration of efficient technologies can reduce CO 2 emissions by 20% to 50%. Of the technologies available, efficient lighting, biomass water heaters, and efficient electronics bring the most benefit when implemented in rural households.

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Section: Model Calibrationmentioning

confidence: 99%

Energy Service Demand Projections and CO2 Reduction Potentials in Rural Households in 31 Chinese Provinces

et al. 2015

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“…There is a dynamic effect, as the load at t does not depend solely on the temperature at t, but also on the temperatures on the previous days (Le Comte & Warren, 1981). Exhaustion effects may appear because of the limited capacity of existing electricity appliances, so there is no further increase in electricity consumption when the temperature exceeds some saturation level (Henley & Peirson, 1997).…”

Section: Temperature Effectsmentioning

confidence: 99%

Forecasting the electricity load from one day to one week ahead for the Spanish system operator

Cancelo¹,

Espasa²,

Grafe³

2008

International Journal of Forecasting

144

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This paper discusses the building process and models used by Red Eléctrica de España (REE), the Spanish system operator, in short term electricity load forecasting. REE's forecasting system consists of one daily model and 24 hourly models with a common structure. There are two types of forecasts of special interest to REE, several days ahead predictions for daily data, and one day ahead hourly forecasts. Accordingly, the forecast accuracy is assessed in terms of their errors. To do this, we analyse historical, real time forecasting errors for daily and hourly data for the year 2006, and report the forecasting performance by day of the week, time of the year and type of day. Other aspects of the prediction problem, like the influence of the errors in predicting the temperature on forecasting the load several days ahead, or the need for an adequate treatment of special days, are also investigated.

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“…Non-parametric models have shown to perform better than parametric ones (Zarnikau, 2003;Henley and Peirson, 1997), but data requirements are very demanding and difficult to be satisfied by a country-level panel. Other solutions have thus been proposed: the use different variables for high and low temperatures (e.g.…”

Section: 2mentioning

confidence: 99%

The Impact of Temperature Change on Energy Demand: A Dynamic Panel Analysis

2007

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in SummaryThis paper presents an empirical study of energy demand, in which demand for a series of energy goods (Gas, Oil Products, Coal, Electricity) is expressed as a function of various factors, including temperature. Parameter values are estimated econometrically, using a dynamic panel data approach. Unlike previous studies in this field, the data sample has a global coverage, and special emphasis is given to the dynamic nature of demand, as well as to interactions between income levels and sensitivity to temperature variations. These features make the model results especially valuable in the analysis of climate change impacts. Results are interpreted in terms of derived demand for heating and cooling. Non-linearities and discontinuities emerge, making it necessary to distinguish between different countries, seasons, and energy sources. Short-and longrun temperature elasticities of demand are estimated.

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Non‐Linearities in Electricity Demand and Temperature: Parametric Versus Non‐Parametric Methods

Cited by 97 publications

References 8 publications

Energy Service Demand Projections and CO2 Reduction Potentials in Rural Households in 31 Chinese Provinces

Energy Service Demand Projections and CO2 Reduction Potentials in Rural Households in 31 Chinese Provinces

Forecasting the electricity load from one day to one week ahead for the Spanish system operator

The Impact of Temperature Change on Energy Demand: A Dynamic Panel Analysis

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