Special Issue "Machine Learning for Energy Forecasting"
Deadline for manuscript submissions: closed (31 October 2020).
Interests: artificial intelligent; machine learning; e-commence; fuzzy system; neuro-fuzzy systems; system optimization
This Special Issue aims to investigate the Machine Learning for Energy Forecasting. Energy forecasting is an essential component of the energy industry. Energy forecasting includes loading forecast, electricity price forecast, wind power forecast, and solar power forecast, etc. Not only can the accurate forecasting support investment profitability analysis and power generation planning, but it also enables smart strategies to be applied to price bidding and risk management. In addition, it can optimize the grid operation. However, building a reliable forecasting solution has always been challenging. The machine learning is one of techniques for energy forecasting. With Machine Learning Forecasting, processors learn from mining loads of energy data without human interference. Extrapolative analysis and algorithms include support vector machines (SVM), least square support vector machines (LSSVM), Recurrent Neural Networks (RNN), Bayesian Neural Network (BNN), CART regression trees, Gaussian Processes (GP), Generalized Regression Neural Networks (GRNN), and Multi-Layer Perceptron (MLP), etc. I am looking for new research based on novel machine learning techniques for Energy Forecasting.
Prof. Dr. Kuo-Ping Lin
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 papers will be 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. Applied Sciences 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 2000 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 Forecasting
- Artificial Intelligence
- Sustainable Energy