Analysing the electricity demand pattern

Gaur, Kajal; Kumar, Harish; Agarwal, Rathour P. K.; Baba, K.V.S.; Soonee, S.K.

doi:10.1109/npsc.2016.7858969

Cited by 41 publications

(15 citation statements)

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“…The influence of the economic sector is also considered in some works [28]. The combined influence of various factors on the forecasting of energy demand in the short, medium or long term is analyzed in [29], [30]. The analysis of the electricity demand profiles is mainly based on hourly data obtained through automatic meter readings [31].…”

Section: State Of the Artmentioning

confidence: 99%

Monthly Electricity Demand Patterns and Their Relationship With the Economic Sector and Geographic Location

et al. 2021

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In a highly competitive and liberalized energy market, where the retail of electricity is open to many potential companies, it is essential to have tools that help make decisions and guide the design of marketing strategies. In this sense, it is essential for retailers to know the behavior of their customers to correctly define their commercial strategies. One of the most commonly used methods for this is the characterization of their consumption profiles. Fortunately, for regulatory reasons, in some countries, the monthly electricity demand of each customer is openly available to any competitor. This paper explores whether this information, especially the economic sector and geographic location of a client, is useful for determining the client's demand profile. Specifically, data on electricity demand in Spain from more than 27 million users and for a period of 3 years are analyzed. For this purpose, the electricity consumption of every client is grouped by month and normalized. The resulting demand profiles are later clustered according to different criteria. The main finding of the research is that the combined information on economic activity and location definitely enables prediction of the demand profile. Additionally, profile quality metrics are defined and obtained for the entire dataset. The resulting profiles have a mean dispersion of 10% and a confidence interval of ±17%. To clarify the use of these metrics, several examples are detailed, showing how this profile information can be used to improve the marketing decision-making process for electricity retailers.INDEX TERMS Energy demand, customer profiling, data engineering, big data applications, statistical learning, pattern analysis.

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Section: State Of the Artmentioning

confidence: 99%

Monthly Electricity Demand Patterns and Their Relationship With the Economic Sector and Geographic Location

et al. 2021

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“…3. In general, the peak load during winter is less than the peak load during summer [21]. In this paper, the total peak load of 175 kW is considered for summer, whereas the total peak load of 170 kW is considered for winter.…”

Section: System Descriptionmentioning

confidence: 99%

Voltage control using smart transformer via dynamic optimal setpoints and limit tolerance in a residential distribution network with PV sources

Manojkumar

Kumar

Ganguly

et al. 2020

IET gener. transm. distrib.

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“…The figure displays a fairly typical power demand curve of a very large, self-sufficient system. [136][137][138] The values in this curve, as well as all the other power values in the figure are normalized by a factor, which makes the minimum power demand = 1. To assure electricity supply at all time, the designed power generation capacity of the SCR system must always be larger than the demand; surplus energy is then used for fuel production, so ξ approaches 1.…”

Section: V3 System Configuration 3-combination Of Pv and Scrmentioning

confidence: 99%

A generalized approach for selecting solar energy system configurations for a wide range of applications

Doron

Karni

Slocum

2019

MRS Energy & Sustainability

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Analysing the electricity demand pattern

Cited by 41 publications

References 0 publications

Monthly Electricity Demand Patterns and Their Relationship With the Economic Sector and Geographic Location

Monthly Electricity Demand Patterns and Their Relationship With the Economic Sector and Geographic Location

Voltage control using smart transformer via dynamic optimal setpoints and limit tolerance in a residential distribution network with PV sources

A generalized approach for selecting solar energy system configurations for a wide range of applications

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