Browsing Τμήμα Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών / Department of Electrical and Computer Engineering by Subject "Machine learning"
Now showing items 1-5 of 5
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Conference Object
Advanced health-state data analytic workflow for utility-scale photovoltaic power plants
(IEEE, 2023)This work aims to present data analytic advances and next-generation workflows for utility-scale photovoltaic (PV) power plant monitoring. The proposed health-state architecture comprises of an integrated and scalable ...
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Article
Characterizing soiling losses for photovoltaic systems in dry climates: a case study in Cyprus
(Elsevier, 2023)Ensuring optimal performance of solar photovoltaic (PV) systems requires the extensive assessment and understanding of losses of different origin that affect these installations. Soiling is a key loss factor influencing ...
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Conference Object
Direct Against Indirect Short-Term Net Load Forecasting Using Machine Learning Principles for Renewable Microgrids
(IEEE Xplore, 2023)Net load forecasting (NLF) is a key component for the efficient operation and management of microgrids at high shares of renewables. Depending on the forecasting strategy followed, NLF is classified as direct or indirect. ...
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Article
Direct Short-Term Net Load Forecasting Based on Machine Learning Principles for Solar-Integrated Microgrids
(IEEE, 2023)Accurate net load forecasting is a cost-effective technique, crucial for the planning, stability, reliability, and integration of variable solar photovoltaic (PV) systems in modern power systems. This work presents a direct ...
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Article
Direct short-term net load forecasting in renewable integrated microgrids using machine learning: a comparative assessment
(Elsevier, 2024)Modern microgrids require accurate net load forecasting (NLF) for optimal operation and management at high shares of renewable energy sources. Machine learning (ML) principles can be used to develop precise and reliable ...