Machine Learning and Big Data Analytics in Energy Infrastructure, including Economic Implications

Dear Colleagues,

Machine learning and big data analytics are playing a big role in energy infrastructure, profit maximization, and resource allocation, both for renewable and non-renewable sources. There are many different economic considerations in all such decisions. Diagnostics, prognostics, and predictive analytics are important aspects of all energy installations which can benefit from machine learning and big data analytics. The key aim of this Special Issue is to present novel theoretical and applicative research developments covering these topics. Therefore, high-quality, not yet published papers from researchers and professionals working in the field of diagnostics and monitoring of energy infrastructure are expected. The proposed papers can deal with detecting winding, bearing or other mechanical faults of the energy installations, and their power converters by means of online and offline, intrusive and non-intrusive, signal-, model- or data-based methods. The proposed approaches can be based on improvements of the traditional time and frequency domain and discrete wavelet transform analysis or modern artificial intelligence-based methods. Papers covering the advanced diagnosis and monitoring methods are strongly welcomed. Further, those dealing with methods to be directly applied in the industrial environment or with comprehensive industrial experiences in the field will be highly appreciated.

Dr. Dipankar Deb
Dr. Moinak Maiti
Prof. Ram Bilas Pachori
Guest Editors

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