Predicting Energy Demand Using Machine Learning: Exploring Temporal and Weather-Related Patterns, Variations, and Impacts

Predicting Energy Demand Using Machine Learning: Exploring Temporal and Weather-Related Patterns, Variations, and Impacts

This study aims to develop models for predicting hourly energy demand in the State of Connecticut, USA from 2011 to 2021 using machine learning algorithms inputted with airport weather stations’ data from the Automated Surface Observing System (ASOS), demand data from ISO New England (ISO...

Full description

Bibliographic Details
Main Authors: Buket Sahin, Kingsley Udeh, David W. Wanik, Diego Cerrai
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Energy demand
machine learning
weather stations
ISO New England
COVID-19
Bayesian optimization
Online Access:https://ieeexplore.ieee.org/document/10445218/

Internet

https://ieeexplore.ieee.org/document/10445218/

Similar Items