Temperature effects on firms’ electricity demand: An analysis of sectorial differences in Spain

Carcedo, Julián Moral; Pérez-García, Julián

doi:10.1016/j.apenergy.2014.12.064

Cited by 42 publications

(18 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…So, with any rise or fall of the air temperature, consumers begin to use air conditioners, and they may violate their obligations in DR and EE programs. [49][50][51] A reduction in the selected capacity of consumers implies that retailers may fail to fulfill their commitments in the capacity market and will be penalized. So retailers should predict this capacity reduction and participate in the capacity market based on their available capacity.…”

Section: Modeling Of Air Temperature Change (Modeling Of For 1 )mentioning

confidence: 99%

Modeling of retailer's behavior for participation in the capacity market

Jalili

Eslami

Moghaddam

et al. 2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary Due to economic reasons, only way for demand side management resources participation in capacity market is their participation by load serving entities, energy service companies, or retailers. To achieve the optimal participation of retailers in the capacity market, it is crucial to model the effective parameters in retailers’ behavior model and how they trade with the independent system operator and the consumers. So a novel method is proposed in this paper for modeling retailers’ behavior, considering demand side management resources availability for their participation in long term commitments. The capacity product of the retailer and its behavior are comprehensively modeled to trade in the capacity market. The competition of retailers is also considered and its effects on the profit of the retailer and consumers are analyzed. A genetic algorithm has been used to solve this problem due to its ability for solving large‐scale real nonlinear Non‐deterministic Polynomial‐time hard (NP‐hard) optimization problems.

show abstract

Section: Modeling Of Air Temperature Change (Modeling Of For 1 )mentioning

confidence: 99%

Modeling of retailer's behavior for participation in the capacity market

Jalili

Eslami

Moghaddam

et al. 2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…It is observed that short cycling electricity consumption is significantly influenced by day type (such as weekday, weekend, official holiday, religious days) and weather conditions. It is seen that meteorological variables such as temperature, precipitation and humidity are influential in the regional consumption of electricity and the meteorological variable which is the most effective is the temperature [4]. Slide.…”

Section: Introductionmentioning

confidence: 99%

Kısa Vadeli Elektrik Tüketiminin Sıcaklığa Bağlı Yapay Sinir Ağları ile Tahmini

Toros

Aydın

2018

European Journal of Science and Technology

View full text Add to dashboard Cite

Today, energy consumption is one of the most important indicators of countries' development levels. Energy, the most important input of social and economic development, is a necessary requirement for the increase of living standard and sustainable development. Electricity is one of the most preferred and consumed energy types because of easy use, easy transportation and clean energy. Electricity consumption varies depending on various social and economic variables such as population, economic growth and gross domestic product as well as on climatic variables such as temperature, precipitation and humidity. The electricity used for heating and cooling needs is bigger in electricity consumption. Weather conditions cause increase and decrease in electricity consumption. Temperature is the meteorological variable with the highest effect. As the comfort temperature gets away from the accepted temperature range, the electricity consumption also increases. The balance of electricity production and consumption is also important in terms of atmospheric disasters. In study, the relation of Turkish electricity consumption with temperature was examined between January 2012 and November 2016. In monthly and seasonal time periods, it was researched how the consumption was changed due to the temperature and how much it was changed and it was aimed to make a more consistent consumption estimation by adding as a temperature input to the consumption estimation model. In the study, short-term electricity consumption estimations were made by using Artificial Neural Network (ANN) method and data groups modeled by Levenberg-Marquardt backpropagation algorithm on MATLAB programing language. The temperature data is produced with a weighted average by consumption amount. The temperatures of 12 provinces with the largest share of consumption in Turkey are weighted according to their consumption rates. In the model weighting, the percentage of effect 1 day before is more than 1 week before and 1 week before is more than 1 year before. For this reason, deviations in the recent history more influence the model. Model results show that, error rates are considered to be reasonable. It is planned to establish the model on a regional basis for future work. When estimating regional electricity demand, a model can be developed by using different meteorological variables. It is predicted that rainfall data will increase the performance of estimates of the inclusion of temperature data in the Bosphorus region, which has a high population density and a high level of residential consumption.

show abstract

“…), appliances, building typehowever the effect of abovementioned is not completely understood [2]. According to many research papers which has been assessed by [4] the temperature which is understood usually as cooling and heating degree-days (CDD, HDD) poses a strict influence on electric energy demand in case of residential sector. However, as other studies pointed out this situation may prevail only for some sectors and even if, its range may be limited to the selected region.…”

Section: Introductionmentioning

confidence: 99%

“…However, as other studies pointed out this situation may prevail only for some sectors and even if, its range may be limited to the selected region. Authors [4] state that in case of Spain there is a total lack of industrial activities electricity demand to the temperature variation.…”

Section: Introductionmentioning

confidence: 99%