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Showing 6 results for Electricity Consumption
Sm Sajjadi, Sm Asadzadeh, Volume 11, Issue 1 (4-2008)
Abstract
The main purpose of this paper is to study the effect of daylight saving time (DST) policy on electricity consumption in Tehran province. A simulation approach based on econometric models is developed. The econometric model used is a set of 24 regression equations called seemingly unrelated regressions (SUR). A simulation approach based on SUR equations is employed to analyze different scenarios. Three DST scenarios are introduced and analyzed: DST, Extended DST and Double DST. Result of simulation approach based on the SUR model using data in years 2004-2006 indicates that the following electricity decreasing and peak-cut-off potentials exist under different scenarios. Under DST scenario there is 162886 MWh decreasing potential in electricity consumption (equal to 0.87 % of first six months consumption). The peak-cut-off potential in this scenario is 102 MW or 1.9 %. Under Extended DST scenario these amounts are a bit more. There is 176732 MWh decreasing potential in electricity consumption (0.82 % of 8 months –from Esfand to Mehrconsumption). In extended DST, the peak can be cut by 97 MW or 1.9 %. In double DST scenario a potential of 68474 MWh (equal to 0.36 % of first six months consumption) exists but under this scenario the peak increases by 482 MW (9.3%). The analysis of DST impact on electricity consumption in Tehran province can be extended to the other provinces in the country using similar data. The simulation tool developed and used in this paper is a useful tool to explore the precise impact of DST on electricity nation wide.
Hadi Dashtaki Hesari, Mohammad Ali Ghazizadeh, Mohammad Ali Zahed, Volume 19, Issue 1 (4-2016)
Abstract
In this article, different kinds of solar cooling for residential usage are investigated. Different parts of all systems are selected according to the current technology and economic calculations based on different parameters are implemented. Besides the economic evaluation of these systems as a substitution for the current cooling systems, their environmental effects from the national point of view are considered and their social acceptance are explained. The systems being studied are chosen from both major classes of solar cooling systems (photovoltaic and thermal) and all the parameters influencing size and price are paid attention to. The other goal of this project is to help policy-makers, who develop the solar industry in the country, in investing in the proper technology in the area of solar cooling. Hence the southern regions of the country, which require the highest cooling load and have the most potential in capturing the solar energy during the year, are selected for analysis. The important point in choosing solar cooling is its high influence on the peak electricity consumption in the country in the hot days of the summer which is among the focus points of the government.
Elahe Tavakoli, Zahra-Sadat Zomorodian, Mohamad Tahsildoost, Mohamadreza Hafezi, Volume 22, Issue 3 (12-2019)
Abstract
The main share of energy consumption in the country and more than a third of its loss is in residential use. Per capita, household electricity consumption is far from the world standard, and the existence of subsidies for energy carriers in Iran, and consequently their low prices, causes occupants to pay less attention to energy consumption. Due to the policy of eliminating subsidies, the effect of improving buildings and occupant behavior on consumption cannot be ignored. The purpose of this study is to investigate the effect of occupant behavior on energy consumption in residential buildings in Isfahan. The statistical populations are 78 typical retrofitted and non-retrofitted residential buildings. Retrofitted buildings are grouped based on the implemented measures into three categories and compared with non-retrofitted units. In addition, the effect of occupants behavior in the studied cases is assessed through questionnaire surveys and simulations (using Design builder software) to determine the impact of behavior change on reducing energy consumption. According to the results, the implemented retrofit measures reduced electricity consumption by up to 4.6%. While occupants behavior (i.e. electrical appliances and lighting use) could save between three to ten times more than retrofit actions.
Seyed Reza Seyedjavadin, Bahman Mostafa Tehrani, Amir Khanlari, Mehdi Hakimi, Volume 23, Issue 4 (3-2021)
Abstract
Household electricity consumption accounts for 33% of total consumption in Iran and any amount of savings in this sector has a significant effect on the total electricity consumption. However, despite all the recommendations and requests to comply with consumption and the high potential of savings in the home sector, but the impact on consumer behavior is not significant and we are still facing the growth of household electricity consumption. Therefore, existing policies and methods to change the consumption pattern of household electricity subscribers have not been very successful in practice. Therefore, it seems necessary to identify, explain and analyze the factors influencing the process of formation of electrical energy consumption behaviors and reconsider this issue. The purpose of this article is to "provide a model for influencing the intention and economical behavior of household electricity subscribers" and with the data approach, the foundation seeks to identify the factors that affect the economical behavior of household electricity consumers.
Mohammad Mirbagherijam, Emad Bani Torfi, Hamideh Moharrami, Volume 26, Issue 4 (2-2024)
Abstract
Predicting daily peak electricity consumption and identifying its determining factors are helpful in balancing electricity supply and demand. In this study, the maximum daily electricity consumption of Iran is modeled and predicted using different approaches in the period from March 20, 2017 to june 6, 2022. 90% of the observations are used to build the model and the rest are used to evaluate the predictive model. Based on the minimum prediction error criterion, the optimal neurons of the hidden layers of the neural network (NN) model were estimated to be 11 in the first layer and 8 in the second layer. The results show that the frequency of the first consumption peak at 11 a.m. and the frequency of the second consumption peak at 9 p.m. are higher than in the other hours. The variables holidays, hours of energy consumption, number of new corona patients, temperature and relative humidity of the air and population have a significant influence on peak electricity consumption in the regression model. The influence of holidays and air temperature is also stronger than other variables. Comparing the peak power consumption prediction results shows that the prediction accuracy of different models and approaches is not the same. The average prediction error percentage of the GLM, NN, and ARIMA models during 187 days (02/12/2021 - 06/06/2022) are 0.0799, 0.0754, and 0.0714, respectively. Therefore, the ARIMA model with the minimum average forecast error is a suitable model for peak consumption forecasting.
Maryam Keyghobadi, Dr Elaheh Sadat Akbarnia, Dr Farzaneh Nezakati Rezapour, Volume 26, Issue 4 (2-2024)
Abstract
One of the influencing factors on energy consumption behavior is having knowledge and awareness in the field of its issues, including electricity generation methods, renewable energies, optimal consumption methods and its environmental consequences. The purpose of this research is to study the level of awareness of Iranian citizens and its effect on electricity consumption behaviors. For this purpose, with a quantitative approach, survey method and online questionnaire technique, we collected the data of household electricity sector subscribers from all over Iran, and finally 1081 individuals completed questionnaires were obtained. Data analysis and interpretation was done with SPSS software. The variable of knowledge and awareness was measured by knowing the comfortable temperature, how to calculate the cost of electricity, peak consumption hours, high consumption devices, the main sources of electricity production and citizens' self-declaration of their level of awareness in energy-related issues. The results showed that the citizens' awareness score is slightly less than 50 (out of 100 points). The behavior variable was measured with twenty items in cooling, lighting, cleaning, washing, etc. It was also asked about the time and amount of consumption of electrical appliances at home. The answers are combined and then the scores are added and standardized in the range of 0 to 100. For the average behavior, a number of 56.48 was obtained. In the correlation analysis between these two variables, Pearson's coefficient was used and the obtained number "0.078" indicates a positive but not very strong relationship between knowledge and awareness and electricity consumption behavior. In the analysis of this finding, by returning to the theories of this field, including theory practice, we find that knowledge and awareness is only one of the elements that shape energy consumption behavior. Other elements such as laws, technologies, infrastructures, norms, skills and meanings also influence the practice variable in combination. Therefore, an important finding of this research is the need to adopt a comprehensive approach to analyze the network of human and non-human actors involved in shaping energy consumption behavior, which in addition to humans, their awareness, beliefs and activities, the role of non-human factors such as technology, infrastructure, tools and facilities is considered as well.
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