Application of Time Series Prediction Models in Chinese Electricity Consumption Analysis

Study focuses on the application of time series prediction models in analyzing electricity consumption in China, aiming to promote the intelligentization and sustainable development of the country's power system. The ARIMA model was chosen for this research, incorporating historical electricity data to forecast future consumption. Through a series of steps including data preprocessing, model construction, and prediction analysis, the results indicate a general growth trend in electricity consumption, influenced by seasonal and cyclical factors. By plotting periodic decomposition charts, the study visually reveals the components and patterns of electricity data, providing scientific decision-making support for the planning, scheduling, and management of the electricity industry.

Authors

Institutions

Publication Details

Journal
Highlights in Science Engineering and Technology
Published
2024-08-19
DOI
https://doi.org/10.54097/m2cwn583
Primary Topic
Energy Load and Power Forecasting
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Application of Time Series Prediction Models in Chinese Electricity Consumption Analysis

Shaoxuan Xie, Yisheng Gao
Highlights in Science Engineering and Technology
Energy Load and Power Forecasting
article

Application of Time Series Prediction Models in Chinese Electricity Consumption Analysis

Shaoxuan Xie, Yisheng Gao
article en

Abstract

Study focuses on the application of time series prediction models in analyzing electricity consumption in China, aiming to promote the intelligentization and sustainable development of the country's power system. The ARIMA model was chosen for this research, incorporating historical electricity data to forecast future consumption. Through a series of steps including data preprocessing, model construction, and prediction analysis, the results indicate a general growth trend in electricity consumption, influenced by seasonal and cyclical factors. By plotting periodic decomposition charts, the study visually reveals the components and patterns of electricity data, providing scientific decision-making support for the planning, scheduling, and management of the electricity industry.

Highlights in Science Engineering and TechnologyVol. 111
Shandong University of Technology (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 89%
Energy Load and Power Forecasting
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.