Electricity consumption modelling: A case of Germany

Catherine, Linh Phuong; Lin, Kuan-Heng; Molnár, Péter

doi:10.1016/j.econmod.2016.02.010

Cited by 43 publications

(12 citation statements)

References 34 publications

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“…The average mean base temperature (blue dotted The mean base temperature is the arithmetic mean for HDD and CDD base temperatures for each year. The average value for the mean base temperature agrees strongly with the value of 18°C, used commonly in literature throughout the past 30 years [5,23,25,[42][43][44], validating the model and results. The slope of the best-fit lines in heating and cooling regions represent the susceptibility in general and the active sensitivity in particular of electricity demand to the increase or decrease in temperature beyond comfort zone.…”

Section: Model Descriptionsupporting

confidence: 83%

“…Even more, some of the literature provide more complex models to predict the impact of temperature fluctuation on electricity demands [10,15,24]. However, models that are more complex do not necessarily provide results that are more efficient [19,25]. Based on that and on the fact that linear piecewise function models are still widely used, this paper will employ a CDD and HDD-based model with optimally formed linear piecewise function.…”

Section: Introductionmentioning

confidence: 99%

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Investigation of the trends of electricity demands in Jordan and its susceptibility to the ambient air temperature towards sustainable electricity generation

et al. 2019

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Background: Efficient production and reliable availability of electricity requires comprehensive understanding of load demand trends to plan and match production with consumption. Although electricity demand depends on a combination of cultural and economic conditions, weather conditions remain as the major driver. With increased capabilities of accurate predictions of weather, the importance of investigating and quantifying its impact on electricity demand becomes obvious. The electrical system in Jordan has been facing several challenges including the failure to respond to increased demands induced by extreme temperatures. This paper covers a clear gap in literature through presenting a detailed investigation of the electricity consumption trends and in identifying the susceptibility of these trends to weather. Methods: This study relies on the statistical processing and analysis, through modeling of hourly electricity demands in Jordan in the period of 10 years between 2007 and 2016. Actual weather data was used employing the degree-day approach. The monthly, daily, and hourly seasonal variation indices were determined. Optimally formulated piecewise functions were used to track the thermal comfort zone and rate of increase in electricity demand for temperatures beyond it for each year. Moreover, the elasticity of polynomial functions was adopted to identify saturation points to thermally map the electricity consumption. Results: The developed models successfully described the relationship between the daily electricity demand and the mean daily ambient temperature. The average comfort zone width was 4°C and the average mean base temperature was 17.9°C. The sensitivity of electricity demand to both high and low temperatures has increased on average, with 11% and 16.4% to hot and cold weather, respectively. Finally, the electricity demand in cooling was found to saturate at 32.9°C, whereas it saturates for heating at 4.7°C. Conclusions: The electricity demand in Jordan observes seasonal trends in a consistent and predictable manner. An optimally formulated piecewise function successfully tracked the thermal comfort zone and the rate of increase in electricity demand for temperatures beyond it for each year of the study period. Finally, saturation heating and cooling temperatures were acquired from the elasticity of the daily electricity demands modeled against daily HDD and CDD.

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Section: Model Descriptionsupporting

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Investigation of the trends of electricity demands in Jordan and its susceptibility to the ambient air temperature towards sustainable electricity generation

et al. 2019

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“…not mention which time-division concept they used for analysing the seasonal electricity profile. Furthermore, the monthly analysis results illustrate that December is having the highest consumption share, which accords with the result of some monthly electricity studies [61,63].…”

Section: Discussionsupporting

confidence: 87%

“…For instance, if we compare the winter and summer seasons to the whole year in Wepro-LPG as shown in Figure 9, where winter is 26.02% and summer is 23.46%, it indicates that the electricity load in winter is almost 3% higher than in summer to the whole year, which is equal to approximately 240 h or about 9 days. In addition, both seasonal profiles indicate Winter as having the highest consumption share, which concurs with the known seasonal pattern in energy demands studies [60][61][62]. In addition, the seasonal analyses based on meteorological is important to be mentioned as some studies did…”

Section: Discussionsupporting

confidence: 81%

Synthesising Residential Electricity Load Profiles at the City Level Using a Weighted Proportion (Wepro) Model

2020

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It is important to understand residential energy use as it is a large energy consumption sector and the potential for change is of great importance for global energy sustainability. A large energy-saving potential and emission reduction potential can be achieved, among others, by understanding energy consumption patterns in more detail. However, existing studies show that it requires many input parameters or disaggregated individual end-uses input data to generate the load profiles. Therefore, we have developed a simplified approach, called weighted proportion (Wepro) model, to synthesise the residential electricity load profile by proportionally matching the city’s main characteristics: Age group, labour force and gender structure with the representative households profiles provided in the load profile generator. The findings indicate that the synthetic load profiles can represent the local electricity consumption characteristics in the case city of Amsterdam based on time variation analyses. The approach is in particular advantageous to tackle the drawbacks of the existing studies and the standard load model used by the utilities. Furthermore, the model is found to be more efficient in the computational process of the residential sector’s load profiles, given the number of households in the city that is represented in the local profile.

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“…For electricity prices, an ARMA(2,1) was specified for the mean equation, with innovations following a Student's t distribution, and a weekday dummy to take into account weekly periodicity. Utilizing a weekday dummy to account for short-term seasonality of electricity prices is a common approach, and examples can be seen in [30,[53][54][55]. Dummy variables are preferred to alternative specifications when analysing seasonal behaviour due to their ease of interpretation and intuition [56].…”

Section: Arch(1)mentioning

confidence: 99%

The Implications of Policy Uncertainty on Solar Photovoltaic Investment

Assereto

Byrne

2020

Energies

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Policy and electricity price uncertainty provide disincentives to investors considering renewable energy investments. While electricity price uncertainty impacts on investment decisions relating to any energy investment, whether renewable or non-renewable, policy uncertainty will affect renewable energy investment decisions to a far greater extent. In this study, we consider the two main sources of uncertainty a solar Photovoltaic (PV) project is exposed to: electricity price uncertainty and policy uncertainty. We focus our analysis on utility-scale solar photovoltaics in the Pennsylvania, Jersey, Maryland Power Pool (PJM) electricity market and the New Jersey Solar Renewable Energy Credit (SREC) market. Using Solar Renewable Energy Credits as a proxy for policy, we find that there is considerable volatility in both electricity prices and policy. In a sample covering eleven years, we implement univariate Generalized Autoregressive Conditional Heteroskedastic (GARCH) and combinations of GARCH models with different weighting schemes and find that combination models provide superior forecasts. In renewable energy markets, policy supports have a significant impact on an investment’s profitability. The implication for policymakers is clear: to foster investment in solar PV, policy stability is critical.

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Electricity consumption modelling: A case of Germany

Cited by 43 publications

References 34 publications

Investigation of the trends of electricity demands in Jordan and its susceptibility to the ambient air temperature towards sustainable electricity generation

Investigation of the trends of electricity demands in Jordan and its susceptibility to the ambient air temperature towards sustainable electricity generation

Synthesising Residential Electricity Load Profiles at the City Level Using a Weighted Proportion (Wepro) Model

The Implications of Policy Uncertainty on Solar Photovoltaic Investment

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