Machine Learning-Based Energy Forecasting and Its Applications
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".
Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 22858
Special Issue Editors
Interests: AI and machine learning; pattern recognition; sensor; knowledge discovery; time-series data analysis and prediction
Special Issues, Collections and Topics in MDPI journals
Interests: electric power system operation; load forecasting; state estimation; economic dispatch and unit commitment
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
Prof. Dr. Jeong-Do Park
Prof. Dr. Neungsoo Park
Guest Editors
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Keywords
- 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
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