Suchergebnisse

Intro -- Preface -- Contents -- About the Editors -- State of the Art -- Machine Learning Techniques for Renewable Energy Forecasting: A Comprehensive Review -- 1 Introduction -- 2 Background of Forecasting Methods -- 2.1 Persistence Models -- 2.2 Physical Models -- 2.3 Statistical Models -- 2.4 Artificial Intelligence (AI) Models -- 3 Research Methodology -- 3.1 Mapping Questions -- 3.2 Search Strings -- 3.3 Selection of Papers -- 3.4 Data Extraction -- 3.5 Analysis and Classification -- 4 Results and Discussion -- 4.1 Overview of the Selected Studies -- 4.2 RQ1: In Which Years, Sources, and Publication Channels Papers Were Published? -- 4.3 RQ2: Which Research Types Are Adopted in Selected Papers? -- 4.4 RQ3: Which Contexts Are Targeted in Selected Papers? -- 4.5 RQ4: What Kinds of Renewable Energy Are Targeted in Selected Papers? -- 4.6 RQ5: Which Domain Fields Are Targeted in Selected Papers? -- 4.7 RQ6: Which Forecasting Task Research Was Used in the Selected Papers? -- 4.8 RQ7: Which Forecasting Period Was Considered in the Selected Papers? -- 4.9 RQ8: Which Machine Learning Models, Data Mining Tasks and Techniques are Used to Deal with Renewable Energy Forecasting? -- 5 Implications for Researchers -- 5.1 RQ1 -- 5.2 RQ2 -- 5.3 RQ3 -- 5.4 RQ4 -- 5.5 RQ5 -- 5.6 RQ6 -- 5.7 RQ7 -- 5.8 RQ8 -- 6 Conclusion -- References -- Machine Learning for Green Smart Homes -- 1 Introduction -- 1.1 A Little History -- 1.2 Where Are We Today? -- 2 Smart Green Homes -- 3 Home Energy Management -- 4 Big Data -- 5 Machine Learning Application to Residential Data -- 5.1 Machine Learning Algorithms -- 6 Energy Modelling -- 7 Use Cases -- 7.1 CENTS -- 7.2 BIM4EEB/BIMcpd -- 7.3 H2020: InterConnect -- 7.4 Retrokit -- 8 Conclusion -- References -- Artificial Intelligence Based Smart Waste Management-A Systematic Review -- 1 Introduction -- 2 Background.