Guest Editors
Dr. Muhammad Hamza Zafar
Department of Engineering Sciences,University of Agder, Norway.
Email: muhammad.h.zafar@uia.no, prof.muhammadzafar@gmail.com
Dr. Filippo Sanfilipo
Department of Engineering Sciences,University of Agder, Norway.
Email: filippo.sanfilippo@uia.no
Dr. Noman Mujeeb Khan
Department of Electrical Engineering, Beaconhouse College, Islamabad, Pakistan.
Email: noman.mujeeb@bic.edu.pk
Summary
Machine Learning plays an important role in energy optimization for new energy solutions by making smarter and yet data-based decisions across the whole energy environment. This will unlock increased efficiency, sustainability and resilience in our energy system for gaining a cleaner and more secure energy future. By using the right tools and techniques, machine learning holds immense potential to accelerate the development and adoption of new energy solutions paving the way for a more sustainable and resilient future. In this environment, tools like python along with libraries like TensorFlow, PyTorch, scikit-learn and pandas, is widely used for data manipulation and model training. Cloud computing platforms like Google Cloud Platform, Amazon Web Services, and Microsoft Azure provide scalable resources for large datasets. On the other hand, techniques like supervised, unsupervised, reinforcement, deep and transfer learning are used to predict energy demand, optimize energy and enhance prediction accuracy. These methods use regression, classification, clustering, reinforcement learning, deep learning and transfer learning to reduce data requirements and training.
With these developments, we can predict that the future of Machine Learning in optimizing new energy solutions is filled with great potential, unlocking a sustainable and efficient energy landscape. However, Machine Learning models may face challenges against data quality, cybersecurity, cost, infrastructure, etc. High-quality and standardized data is needed for accurate training, while complex models raise concerns about bias and fairness. Also, Cybersecurity measures are crucial and may require resources creating barriers for adoption more in developing countries. Despite the challenges, the potential of ML to revolutionize the energy sector is undeniable.
We plan to organise this Special Issue by providing a platform for researchers and academicians to give their ideas as an research paper on this intersection of machine learning and energy optimization, aiming to showcase studies that push the boundaries of current knowledge.
The included topics are not limited to:
·Machine Learning for Predictive Maintenance in Energy Infrastructure
·Optimization of Smart Grids through Advanced Analytics
·Real-Time Demand Forecasting Using Machine Learning
·Explainable AI in Energy Consumption Modeling
·Cybersecurity in Machine Learning-Enabled Energy Systems
·Data Quality and Management in Energy Optimization Models
·Renewable Energy Forecasting with Artificial Intelligence
·Energy-Efficient Building Management Systems with ML
·Decentralized Energy Systems and Machine Learning
·Integration of Internet of Things (IoT) in Smart Energy Solutions
·Sustainable Transportation and Machine Learning in Energy Efficiency
·Adaptive Learning Algorithms for Dynamic Energy Markets
·Carbon Footprint Reduction through ML-Driven Strategies
Keywords
Machine Learning, Energy Systems, Energy Consumption Modeling, AI,Energy Efficiency
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