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Special Issue "Machine Learning-Based Energy Forecasting and Its Applications II"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 871

Special Issue Editors

Department of Computer Engineering, Jeju National University, Jeju-si 63243, Republic of Korea
Interests: AI and machine learning; pattern recognition; sensor; knowledge discovery; time-series data analysis and prediction
Special Issues, Collections and Topics in MDPI journals
Center for Cyber-Physical Systems, EECS Department, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates
Interests: cyber security; IoT security; cloud security; security for big data analytics; AI for cyber security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The main aim of this Special Issue is to present a forum for researchers comprising the entire range of artificial intelligence and machine-learning-based applications in the energy sector.

The world’s energy sector is facing growing challenges, such as a demand and efficiency increase, supply and demand pattern change, and the absence of a best management analysis. In developing countries, this challenge is even more intense. Transferring data of the energy sector to artificial intelligence can gradually solve this problem. Artificial intelligence algorithms can analyze equipment data, solve problems, and then save money, time, and life. Artificial intelligence applications are also used for smart energy consumption. Modern homes with machine learning algorithms can automatically respond to fluctuations in electricity prices and control energy usage. Systems based on machine learning can help energy suppliers to prepare to keep pace with fluctuating renewable energy supplies. To reduce interest in low-emission energy and oil dependence, solar PV, wind farms, and marine energy systems increase their installed capacity worldwide. Artificial intelligence algorithms can continuously improve monitoring, analyze energy consumption, discover new problems, and perform analysis to improve performance. Price optimization models use the power of neural networks to predict energy demand and create improved price recommendations to help energy companies to achieve their goals. Hence, artificial intelligence and machine learning can play a crucial role in practically managing the challenges of the energy sector.

In this Special Issue, we would like to encourage people to contribute their latest developments and ideas and review articles on machine-learning-based energy forecasting and its applications. This Special Issue will focus on essential AI-based applications in the energy sector. However, it is not limited to the following:

  • Supply and demand patterns variations;
  • Management analysis of energy sector;
  • Optimization of renewable energy using machine learning;
  • Forecasting model for wind speed and solar radiations;
  • AI to overwhelm future energy problems;
  • Fluctuations in electricity prices and control energy usage;
  • Predictive models for smart grids;
  • Forecasting of PV power generation;
  • Electricity market price prediction using advanced deep learning;
  • Ensemble forecasting models;
  • Reinforcement learning and predictive control for smart energy systems;
  • Data mining applications in understanding electricity consumers;
  • Hybrid and combined models.

We encourage you to submit your original work to this issue and look forward to receiving your distinguished research.

Prof. Dr. Yungcheol Byun
Dr. Chan Yeob Yeun
Guest Editors

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.


  • artificial intelligence and machine learning in energy
  • forecasting energy consumption
  • energy informatics
  • renewable energy generation and prediction
  • deep learning and renewable energy
  • time series forecasting
  • wind, solar, and wave energy
  • electricity price forecasting using machine learning
  • PV system and machine learning
  • energy feature engineering
  • energy and time series data analysis
  • ensemble model for energy
  • machine learning and its applications for energy
  • big data and machine learning for energy
  • predictive analytics
  • machine-learning-based sustainability in energy
  • deep neural networks and regression analysis for energy

Published Papers (1 paper)

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A Hybrid Feature Pyramid CNN-LSTM Model with Seasonal Inflection Month Correction for Medium- and Long-Term Power Load Forecasting
Energies 2023, 16(7), 3081; https://doi.org/10.3390/en16073081 - 28 Mar 2023
Viewed by 685
Accurate medium- and long-term power load forecasting is of great significance for the scientific planning and safe operation of power systems. Monthly power load has multiscale time series correlation and seasonality. The existing models face the problems of insufficient feature extraction and a [...] Read more.
Accurate medium- and long-term power load forecasting is of great significance for the scientific planning and safe operation of power systems. Monthly power load has multiscale time series correlation and seasonality. The existing models face the problems of insufficient feature extraction and a large volume of prediction models constructed according to seasons. Therefore, a hybrid feature pyramid CNN-LSTM model with seasonal inflection month correction for medium- and long-term power load forecasting is proposed. The model is constructed based on linear and nonlinear combination forecasting. With the aim to address the insufficient extraction of multiscale temporal correlation in load, a time series feature pyramid structure based on causal dilated convolution is proposed, and the accuracy of the model is improved by feature extraction and fusion of different scales. For the problem that the model volume of seasonal prediction is too large, a seasonal inflection monthly load correction strategy is proposed to construct a unified model to predict and correct the monthly load of the seasonal change inflection point, so as to improve the model’s ability to deal with seasonality. The model proposed in this paper is verified on the actual power data in Shaoxing City. Full article
(This article belongs to the Special Issue Machine Learning-Based Energy Forecasting and Its Applications II)
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