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Related Experiment Video

Updated: May 8, 2025

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

Forecasting for electricity demand utilizing enhanced inception-V4 using improved Osprey optimization.

Suhua Chen¹, Xu Fang², Majid Khayatnezhad³

¹School of Electrical Engineering, Xuchang University, Xuchang, 461000, Henan, China. heroncsh216@163.com.

Scientific Reports

|December 27, 2024

Summary

This summary is machine-generated.

This study reveals that weather significantly impacts electricity demand, with temperature being a key driver. An advanced OOI-Inception-V4 model accurately forecasts energy needs, crucial for future planning.

Keywords:

Electricity demand Heating and cooling systems Inception-V4 model Modified Osprey Optimizer The meteorological factors

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Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

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Published on: February 14, 2025

Area of Science:

Environmental Science
Energy Systems
Data Science

Background:

Electricity demand is influenced by weather patterns.
Accurate forecasting is essential for energy management and policy.
Traditional methods often lack precision in capturing complex weather impacts.

Purpose of the Study:

To investigate the influence of temperature, relative humidity, and wind speed on electricity demand.
To develop and validate a novel hybrid model for improved electricity demand forecasting.
To project future electricity demand trends based on weather-climate interactions.

Main Methods:

Utilized an Enhanced Inception-V4 model for data processing and factor identification.
Employed an Improved Osprey Optimizer to fine-tune model parameters.
Validated the OOI-Inception-V4 model using historical data from 2003 to 2023.

Main Results:

The OOI-Inception-V4 model demonstrated superior accuracy compared to traditional forecasting techniques.
Temperature was identified as a primary factor influencing electricity demand, especially during extreme weather.
The model accurately captured demand variations linked to heating and cooling system usage.

Conclusions:

Advanced predictive models like OOI-Inception-V4 significantly enhance electricity demand forecasting.
Weather conditions, particularly temperature, have a profound impact on energy consumption.
Findings highlight the need for proactive energy policies and infrastructure development to meet projected demand increases.