Paper Status Tracking
Contact us
[email protected]
Click here to send a message to me 3275638434
Paper Publishing WeChat

Article
Affiliation(s)

1. Finance School of Business, Technology, and Engineering, National University, San Diego 92110, California, USA
2. Ageno School of Business, Golden Gate University, San Francisco 94105, California, USA

ABSTRACT

Accurate energy demand forecasting is crucial in today’s rapidly electrifying world with decentralized systems and integrated renewables. Traditional models struggle with the dynamic complexities, but AI (artificial intelligence), particularly ML (machine learning) and DL (deep learning), offers transformative solutions. This article explores how AI enhances forecasting accuracy, enables real-time adaptability, and supports strategic energy management. It examines the synergy between AI, IoT(Internet of Things) devices, and smart grids in generating predictive and prescriptive insights. Through case studies, we analyze the benefits and challenges of deploying AI in this domain, including data quality, model explainability, and infrastructure needs. Ultimately, AI emerges as a key enabler for the resilient, data-driven energy systems required to meet modern society’s evolving demands and achieve a sustainable future.

KEYWORDS

Energy demand forecasting, AI, ML, smart grid, time-series prediction, DLmodels, IoT, renewable energy integration, real-time energy analytics, sustainable energy planning.

Cite this paper

References

About | Terms & Conditions | Issue | Privacy | Contact us
Copyright © 2001 - David Publishing Company All rights reserved, www.davidpublisher.com
3 Germay Dr., Unit 4 #4651, Wilmington DE 19804; Tel: 001-302-3943358 Email: [email protected]