Machine Learning for Energy Load Forecasting

Dear Colleagues,

This Special Issue on “Machine Learning for Energy Load Forecasting” in Energies seeks to showcase the latest advancements in the application of machine learning (ML) techniques to predict energy demands, a critical component for enhancing the sustainability and efficiency of global energy systems. This edition calls for papers that break new ground in ML methodologies, tackle the intricacies of energy forecasting, and demonstrate the practical application of these technologies in real-world settings.

The scope of this Special Issue encompasses a wide array of topics, including the development of innovative ML algorithms for precise energy load forecasting, strategies for the integration of renewable energy sources using predictive models, and the exploration of deep learning techniques to decode complex energy consumption patterns. Contributions that provide a comparative analysis of ML approaches or offer case studies illustrating the implementation challenges and successes in smart grids, sustainable urban environments, and energy-efficient infrastructure are particularly encouraged.

Dr. Behnam Askarian
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• machine learning
• energy load forecasting
• renewable energy integration
• deep learning applications
• smart grids
• energy management
• real-time energy demand prediction
• energy efficiency