scholarly journals An Analysis of the Price Elasticity of Electricity Demand and Price Reform in the Korean Residential Sector Under Block Rate Pricing

2015 ◽  
Vol 24 (2) ◽  
pp. 365-410
Author(s):  
Ha-Hyun Jo ◽  
Min-Woo Jang
Keyword(s):  
Price Elasticity ◽  
Electricity Demand ◽  
Residential Sector ◽  
Price Reform

scholarly journals Testing for Changes in the Price Elasticity of Residential Electricity Demand

1986 ◽  
Vol 15 (2) ◽  
pp. 123-129 ◽  
Author(s):  
Thomas H. Stevens ◽  
Gail Adams
Keyword(s):  
Public Policy ◽  
Price Elasticity ◽  
Forecast Accuracy ◽  
Electricity Demand ◽  
Recent Period ◽  
Residential Sector ◽  
Real Price ◽  
Long Run ◽  
Residential Electricity

The demand for electricity in the residential sector is estimated to have become less elastic for the recent period of rising real prices as compared to earlier periods of stable or falling real price. Several possible reasons for this are investigated and we conclude that demand appears to be asymmetric with respect to price in both the short and long run. We then examine whether or not this is an important factor for forecast accuracy and public policy.

Determinants of residential electricity demand: evidence from Japan

2016 ◽  
Vol 10 (4) ◽  
pp. 546-560 ◽  
Author(s):  
Akihiro Otsuka ◽  
Shoji Haruna
Keyword(s):  
Price Elasticity ◽  
Income Elasticity ◽  
Electricity Demand ◽  
Content Type ◽  
Residential Sector ◽  
Demand Growth ◽  
Electricity Rates ◽  
Residential Electricity ◽  
Using Data ◽  
The Impact

Purpose This paper aims to estimate electricity demand functions in Japan’s residential sector. Design/methodology/approach The authors use a partial adjustment model and empirically analyze regional residential electricity demand by using data on 47 Japanese prefectures. Findings The results reveal that the price elasticity of residential electricity demand during the analytical period (1990-2010) is remarkably different among prefectures, depending on the magnitude of floor space per household. In addition, this study finds that price elasticity is high compared with income elasticity, implying that residential electricity demand changes with rates. Furthermore, an analysis of factors influencing electricity demand in the residential sector shows that increasing electricity demand growth in each region can be attributable mainly to declining electricity rates and increasing number of households. Research limitations/implications These results suggest that monitoring the electricity rates and the number of households is important for forecasting future residential electricity demand at region. Originality/value The study considers the impact of the number of households on overall electricity demand and identifies other factors contributing to growth in residential electricity demand. The findings can be used to derive projections for future electricity demand.

A meta-analysis on the price elasticity and income elasticity of residential electricity demand

2018 ◽  
Vol 201 ◽  
pp. 169-177 ◽  
Author(s):  
Xing Zhu ◽  
Lanlan Li ◽  
Kaile Zhou ◽  
Xiaoling Zhang ◽  
Shanlin Yang
Keyword(s):  
Price Elasticity ◽  
Income Elasticity ◽  
Meta Analysis ◽  
Electricity Demand ◽  
Residential Electricity

scholarly journals A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity

2020 ◽  
Vol 12 (8) ◽  
pp. 3103 ◽  
Author(s):  
Hyojoo Son ◽  
Changwan Kim
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Power System ◽  
Electricity Demand ◽  
Efficient Production ◽  
Power System Planning ◽  
System Planning ◽  
Residential Sector ◽  
Use Of Resources ◽  
Proposed Model

Forecasting electricity demand at the regional or national level is a key procedural element of power-system planning. However, achieving such objectives in the residential sector, the primary driver of peak demand, is challenging given this sector’s pattern of constantly fluctuating and gradually increasing energy usage. Although deep learning algorithms have recently yielded promising results in various time series analyses, their potential applicability to forecasting monthly residential electricity demand has yet to be fully explored. As such, this study proposed a forecasting model with social and weather-related variables by introducing long short-term memory (LSTM), which has been known to be powerful among deep learning-based approaches for time series forecasting. The validation of the proposed model was performed using a set of data spanning 22 years in South Korea. The resulting forecasting performance was evaluated on the basis of six performance measures. Further, this model’s performance was subjected to a comparison with the performance of four benchmark models. The performance of the proposed model was exceptional according to all of the measures employed. This model can facilitate improved decision-making regarding power-system planning by accurately forecasting the electricity demands of the residential sector, thereby contributing to the efficient production and use of resources.

scholarly journals System Dynamics Modeling of Lighting Electricity Demand in the Urban Residential Sector of Nepal

2016 ◽  
Vol 23 (1-2) ◽  
pp. 33-54 ◽  
Author(s):  
Iswor Bajracharya ◽  
Nawraj Bhattarai
Keyword(s):  
System Dynamics ◽  
Electricity Consumption ◽  
Electricity Demand ◽  
Point Of View ◽  
Incandescent Lamp ◽  
Reference Scenario ◽  
System Dynamics Modeling ◽  
Dynamics Modeling ◽  
Residential Sector ◽  
Expansion Planning

A significant portion of the total electricity is consumed in the residential sector of Nepal, mainly for lighting purpose. In this study, a model has been developed using the concept of system dynamics to analyze the dynamics of the changes in the urban residential lighting electricity demand up to the year 2030. A system dynamics modeling tool, Venism, has been used for this purpose. This study is useful for the utilities of companies for the power capacity expansion planning. Altogether three different scenarios have been developed. They are Reference Scenario (Ref), LED Lamp (LL) Scenario and Incandescent Lamp Remove (ILR) Scenario. The study has shown that lighting electricity consumption has already been in the decreasing trend due to the increasing use of Clear Fluorescent Lamp (CFL) and will be the minimum somewhere in the year between 2021 and 2022. Only a small portion of the total electricity will be consumed for lighting the household in the urban residential sector of Nepal in the coming decade. Therefore, government should focus the urban energy efficiency program for other uses of electricity such as cooking, water heating and water pumping etc. so that a significant amount of electricity can be saved in the urban households of Nepal. This study has also shown that there is no difference between the use of CFL and LED lamps from the energy saving point of view. Therefore, like the case of incandescent lamp and CFL, there is no need to encourage the people to buy LED lamp instead of CFL.The Journal of Development and Administrative Studies (JODAS)Vol. 23(1-2), pp. 33-54

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