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ORCIDDARE@PKDD/ECML 2017: Skopje, Macedonia
- Wei Lee Woon, Zeyar Aung, Oliver Kramer, Stuart E. Madnick:
Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy - 5th ECML PKDD Workshop, DARE 2017, Skopje, Macedonia, September 22, 2017, Revised Selected Papers. Lecture Notes in Computer Science 10691, Springer 2017, ISBN 978-3-319-71642-8 - Diana Manjarres
, Ricardo Alonso, Sergio Gil-Lopez, Itziar Landa-Torres:
Solar Energy Forecasting and Optimization System for Efficient Renewable Energy Integration. 1-12 - Nikolaos Nikolaou, Efstratios Batzelis, Gavin Brown:
Gradient Boosting Models for Photovoltaic Power Estimation Under Partial Shading Conditions. 13-25 - Jaroslav Loebl, Helmut Posch, Viera Rozinajová:
Multi-objective Optimization for Power Load Recommendation Considering User's Comfort. 26-32 - Dina Fawzy, Sherin M. Moussa, Nagwa L. Badr:
An Approach for Erosion and Power Loss Prediction of Wind Turbines Using Big Data Analytics. 33-46 - Adhra Ali, Hussain Kazmi:
Minimizing Grid Interaction of Solar Generation and DHW Loads in nZEBs Using Model-Free Reinforcement Learning. 47-58 - Maël Guillemé, Laurence Rozé, Véronique Masson, Cérès Carton, René Quiniou, Alexandre Termier:
Improving Time-Series Rule Matching Performance for Detecting Energy Consumption Patterns. 59-71 - Mehmet Baris Özkan, Umut Guvengir, Dilek Küçük, Ali Unver Secen, Serkan Buhan, Turan Demirci, Abdullah Bestil, Ceyda Er Koksoy, Pinar Karagoz:
Probabilistic Wind Power Forecasting by Using Quantile Regression Analysis. 72-82 - Janosch Henze, Tanja Kneiske, Martin Braun, Bernhard Sick:
Identifying Representative Load Time Series for Load Flow Calculations. 83-93 - Astrid Dahl, Edwin V. Bonilla:
Scalable Gaussian Process Models for Solar Power Forecasting. 94-106 - Khawla Al Dhaheri, Wei Lee Woon, Zeyar Aung:
Wind Speed Forecasting Using Statistical and Machine Learning Methods: A Case Study in the UAE. 107-120 - Alejandro Catalina, José R. Dorronsoro:
NWP Ensembles for Wind Energy Uncertainty Estimates. 121-132
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