Saturated Demand Forecast of Regional Power Grid Based on Amended Self-Adaptive Logistic Model: A Case Study of East China

Yang, Tai-Hua; Wang, Zhuozhi; Wei, Qiming; Gao, Yuqi

doi:10.1109/access.2020.3046105

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…The recent pandemic has also been analyzed using changes in electricity demand patterns [19], [20]. Most authors characterize energy demand based solely on the load curves [21], although some other studies also incorporate the influence of climatic variables [22]- [24], sociodemographic factors [25], aggregated [26] or disaggregated [27] global economic activity, and electricity usage. The influence of the economic sector is also considered in some works [28].…”

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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