Time-series clustering and forecasting household electricity demand using smart meter data
This study forecasts electricity consumption in a smart grid environment. We present a bottom-up prediction method using a combination of forecasting values based on time-series clustering using advanced metering infrastructure (AMI) data, one of the core smart grid technologies. Remote data meterin...
Main Authors: | Hyojeoung Kim, Sujin Park, Sahm Kim |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2023-12-01
|
Series: | Energy Reports |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484723002731 |
Similar Items
-
Smart Meters Time Series Clustering for Demand Response Applications in the Context of High Penetration of Renewable Energy Resources
by: Santiago Bañales, et al.
Published: (2021-06-01) -
Smart Meter Forecasting from One Minute to One Year Horizons
by: Luca Massidda, et al.
Published: (2018-12-01) -
Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters
by: Diogo M. F. Izidio, et al.
Published: (2021-03-01) -
Time Series Clustering of Electricity Demand for Industrial Areas on Smart Grid
by: Heung-gu Son, et al.
Published: (2020-05-01) -
Short-term Forecast of Hourly Electricity Demand in Iran Using a Forecast Combination Method
by: Seyed Farshad Fatemi Ardestani, et al.
Published: (2020-02-01)