Modeling framework for estimating impacts of climate change on electricity demand at regional level: Case of Greece

Mirasgedis, S.; Sarafidis, Y.; Georgopoulou, E.; Kotroni, Vassiliki; Lagouvardos, K.; Lalas, D. P.

doi:10.1016/j.enconman.2006.10.022

Cited by 131 publications

(65 citation statements)

References 18 publications

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“…They showed that effects of climate change (and the associated uncertainties) are not limited to the energy performance of buildings and are extendable to energy systems and infrastructure. Impacts of climate change on the energy network/system have been investigated by some other researchers as well; Mirasgedis et al [43] assessed the sensitivity of electricity demand in the Greek interconnected power system to climate change and socio-economy, using a multiple regression model. They used RCM climate data for the monthly time step and investigated the consequences of the extreme conditions in that scale for 30-year periods.…”

Section: Introductionmentioning

confidence: 99%

“…Uncertainties due to climate change have been considered in several works (e.g. [17,33,[39][40][41][42][43]), some combined with other important uncertainty factors. For example Zhou et al [44] studied uncertainties due to climate change, policy, population and GDP growth in modeling the effects of climate change on the U.S. state-level building energy demands.…”

Section: Introductionmentioning

confidence: 99%

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Making energy simulation easier for future climate – Synthesizing typical and extreme weather data sets out of regional climate models (RCMs)

Nik

2016

Applied Energy

134

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Making energy simulation easier for future climate – Synthesizing typical and extreme weather data sets out of regional climate models (RCMs)

Nik

2016

Applied Energy

134

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“…Thus, the temperature fluctuation in time series is used to examine its impact on HEC for the given region. The impact of temperature variations on energy consumption for heating and cooling is the most sensitive [10,11,32,34]. In this study, to reflect the regional differences, the temperature is regarded as the spatial function of energy consumption.…”

Section: Discussionmentioning

confidence: 99%

Factors Influencing the Spatial Difference in Household Energy Consumption in China

Ding

Niu

et al. 2016

Sustainability

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Abstract:What factors determine the spatial heterogeneity of household energy consumption (HEC) in China? Can the impacts of these factors be quantified? What are the trends and characteristics of the spatial differences? To date, these issues are still unclear. Based on the STIRPAT model and panel dataset for 30 provinces in China over the period 1997-2013, this paper investigated influences of the income per capita, urbanization level and annual average temperature on HEC, and revealed the spatial effects of these influencing factors. The results show that the income level is the main influencing factor, followed by the annual average temperature. There exists a diminishing marginal contribution with increasing income. The influence of urbanization level varies according to income level. In addition, from the eastern region to western region of China, variances largely depend upon economic level at the provincial level. From the northern region to southern region, change is mainly caused by temperature. The urbanization level has more significant impact on the structure and efficiency of household energy consumption than on its quantity. These results could provide reference for policy making and energy planning.

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“…The authors in [3] illustrated that the global warming can cause up to 6% additional demand consumption required annually in Greece. In [4], the authors showed that the change of the peak electricity demand in Thailand can be increased by more than 15% at the end of the century.…”

Section: Introductionmentioning

confidence: 99%

“…In [4], the authors showed that the change of the peak electricity demand in Thailand can be increased by more than 15% at the end of the century. While in [3] and [4], only temperature was in the consideration, the authors in [5] believed the other climatic variables such as relative humidity, and wind speed also have strong influence to electricity consumption by analysing the data in eight states of USA. In [6], different dependent patterns of electricity demand on climatic variables in different seasons in the states of NSW, Australia were represented in four different regression models.…”

Section: Introductionmentioning

confidence: 99%

Assessing the influence of climatic variables on electricity demand

Muttaqi

Agalgaonkar

2014

2014 IEEE PES General Meeting | Conference &Amp; Exposition

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The electricity demand is significantly dependent on the weather information. Such weather information is comprised of different climatic variables such as temperature, humidity, wind speed, evaporation, rain fall and solar exposure which constantly change. Therefore, analysing the impacts of these variables on demand is necessary for predicting the future change in demand. In this paper, the cooling and heating degree days are utilised to capture the relationship between the per capita demand to temperature, which is one of the key climatic variables. In addition, Pearson correlation analysis has been employed to investigate the interdependency between different climatic variables and electricity demand. Finally, back-ward elimination based multiple regression is used to exclude non-significant climatic variables and evaluate the sensitivity of significant variables to the electricity demand. A case study has been reported in this paper by acquiring the data from the state of New South Wales, Australia. The results reveal that the climatic variables such as heating degree days, humidity, evaporation, and wind speed predominantly affect the electricity demand of the state of New South Wales. Abstract -The electricity demand is significantly dependent on the weather information. Such weather information is comprised of different climatic variables such as temperature, humidity, wind speed, evaporation, rain fall and solar exposure which constantly change. Therefore, analysing the impacts of these variables on demand is necessary for predicting the future change in demand. In this paper, the cooling and heating degree days are utilised to capture the relationship between the per capita demand to temperature, which is one of the key climatic variables. In addition, Pearson correlation analysis has been employed to investigate the interdependency between different climatic variables and electricity demand. Finally, back-ward elimination based multiple regression is used to exclude nonsignificant climatic variables and evaluate the sensitivity of significant variables to the electricity demand. A case study has been reported in this paper by acquiring the data from the state of New South Wales, Australia. The results reveal that the climatic variables such as heating degree days, humidity, evaporation, and wind speed predominantly affect the electricity demand of the state of New South Wales.

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Modeling framework for estimating impacts of climate change on electricity demand at regional level: Case of Greece

Cited by 131 publications

References 18 publications

Making energy simulation easier for future climate – Synthesizing typical and extreme weather data sets out of regional climate models (RCMs)

Making energy simulation easier for future climate – Synthesizing typical and extreme weather data sets out of regional climate models (RCMs)

Factors Influencing the Spatial Difference in Household Energy Consumption in China

Assessing the influence of climatic variables on electricity demand

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