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Annual energy outlook / / Energy Information Administration, Office of Energy Markets and End Use, U.S. Department of Energy [[electronic resource]]
| Annual energy outlook / / Energy Information Administration, Office of Energy Markets and End Use, U.S. Department of Energy [[electronic resource]] |
| Pubbl/distr/stampa | Washington, D.C. : , : Energy Information Administration, Office of Energy Markets and End Use, U.S. Department of Energy : , 1983- |
| Descrizione fisica | 1 online resource |
| Disciplina | 333.79/0973 |
| Soggetto topico |
Power resources - United States - Forecasting
Power resources - Forecasting Energy consumption - United States - Forecasting Energy consumption - Forecasting Forecasting Sectoral analysis Supply and demand Compiled data Prices ENERGY FORECASTS ENERGY STATISTICS UNITED STATES |
| Soggetto genere / forma |
Internet resource
Periodicals. Statistics. |
| Formato | Materiale a stampa  |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910137199703321 |
| Washington, D.C. : , : Energy Information Administration, Office of Energy Markets and End Use, U.S. Department of Energy : , 1983- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Annual energy outlook / / Energy Information Administration, Office of Energy Markets and End Use, U.S. Department of Energy
| Annual energy outlook / / Energy Information Administration, Office of Energy Markets and End Use, U.S. Department of Energy |
| Pubbl/distr/stampa | Washington, D.C. : , : Energy Information Administration, Office of Energy Markets and End Use, U.S. Department of Energy : , 1983- |
| Descrizione fisica | 1 online resource |
| Disciplina | 333.79/0973 |
| Soggetto topico |
Power resources - United States - Forecasting
Power resources - Forecasting Energy consumption - United States - Forecasting Energy consumption - Forecasting Forecasting Sectoral analysis Supply and demand Compiled data Prices ENERGY FORECASTS ENERGY STATISTICS UNITED STATES |
| Soggetto genere / forma |
Periodicals.
Statistics. |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910895709103321 |
| Washington, D.C. : , : Energy Information Administration, Office of Energy Markets and End Use, U.S. Department of Energy : , 1983- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Asia's growing hunger for energy : U.S. policy and supply opportunities : hearing before the Subcommittee on Asia and the Pacific of the Committee on Foreign Affairs, House of Representatives, One Hundred Fourteenth Congress, second session, September 8, 2016
| Asia's growing hunger for energy : U.S. policy and supply opportunities : hearing before the Subcommittee on Asia and the Pacific of the Committee on Foreign Affairs, House of Representatives, One Hundred Fourteenth Congress, second session, September 8, 2016 |
| Pubbl/distr/stampa | Washington : , : U.S. Government Publishing Office, , 2016 |
| Descrizione fisica | 1 online resource (iii, 51 pages) : facsimiles |
| Soggetto topico |
Energy consumption - Forecasting
Energy consumption - Asia Energy security - Asia Clean energy - Asia Energy security - Pacific Area Energy consumption - Pacific Area Energy policy - United States Clean energy - Pacific Area Clean energy industries - United States - Marketing |
| Soggetto genere / forma | Legislative hearings. |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Asia's growing hunger for energy |
| Record Nr. | UNINA-9910707855003321 |
| Washington : , : U.S. Government Publishing Office, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Biocombustibili e biocarburanti : soluzioni, tecnologie, agevolazioni / a cura di Alessandro Bordin
| Biocombustibili e biocarburanti : soluzioni, tecnologie, agevolazioni / a cura di Alessandro Bordin |
| Pubbl/distr/stampa | [s. l.] : IPSOA : Indicitalia, 2007 |
| Descrizione fisica | x, 360 p. : ill. ; 24 cm |
| Disciplina | 333.79 |
| Altri autori (Persone) | Bordin, Alessandro |
| Collana | Sviluppo sostenibile ; 1 |
| Soggetto topico |
Power resources - Italy
Energy consumption - Italy Energy policy - Italy Energy consumption - Forecasting Refractory materials |
| ISBN | 9788821726132 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | ita |
| Record Nr. | UNISALENTO-991003002459707536 |
| [s. l.] : IPSOA : Indicitalia, 2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Salento | ||
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Clean energy [[electronic resource] /] / Ronald M. Dell, David A.J. Rand
| Clean energy [[electronic resource] /] / Ronald M. Dell, David A.J. Rand |
| Autore | Dell Ronald |
| Pubbl/distr/stampa | Cambridge, : Royal Society of Chemistry, c2004 |
| Descrizione fisica | 1 online resource (360 p.) |
| Disciplina | 333.79 |
| Altri autori (Persone) | RandD. A. J <1942-> (David Anthony James) |
| Collana | RSC clean technology monographs |
| Soggetto topico |
Energy policy
Renewable energy sources Energy consumption - Forecasting Power resources - Environmental aspects Pollution prevention |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-84755-055-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Energy production and use -- Clean fuels -- Electricity generation -- Renewable energy, thermal -- Renewable energy, electrical -- Why store electricity? -- Physical techniques for storing energy -- Hydrogen energy -- Battery storage -- Electric propulsion -- Towards 2020. |
| Record Nr. | UNINA-9910454077903321 |
Dell Ronald
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| Cambridge, : Royal Society of Chemistry, c2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Clean energy [[electronic resource] /] / Ronald M. Dell, David A.J. Rand
| Clean energy [[electronic resource] /] / Ronald M. Dell, David A.J. Rand |
| Autore | Dell Ronald |
| Pubbl/distr/stampa | Cambridge, : Royal Society of Chemistry, c2004 |
| Descrizione fisica | 1 online resource (360 p.) |
| Disciplina | 333.79 |
| Altri autori (Persone) | RandD. A. J <1942-> (David Anthony James) |
| Collana | RSC clean technology monographs |
| Soggetto topico |
Energy policy
Renewable energy sources Energy consumption - Forecasting Power resources - Environmental aspects Pollution prevention |
| ISBN | 1-84755-055-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Energy production and use -- Clean fuels -- Electricity generation -- Renewable energy, thermal -- Renewable energy, electrical -- Why store electricity? -- Physical techniques for storing energy -- Hydrogen energy -- Battery storage -- Electric propulsion -- Towards 2020. |
| Record Nr. | UNINA-9910782761603321 |
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Dell Ronald
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| Cambridge, : Royal Society of Chemistry, c2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Data-Driven Energy Management and Tariff Optimization in Power Systems : Shaping the Future of Electricity Distribution Through Analytics
| Data-Driven Energy Management and Tariff Optimization in Power Systems : Shaping the Future of Electricity Distribution Through Analytics |
| Autore | Arasteh Hamidreza |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (320 pages) |
| Disciplina | 333.7932 |
| Altri autori (Persone) |
SianoPierluigi
MoslemiNiki GuerreroJosep M |
| Soggetto topico |
Electric power systems - Management
Energy consumption - Forecasting |
| ISBN |
1-394-29030-6
1-394-29028-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- About the Editors -- List of Contributors -- Preface -- Chapter 1 Fundamentals of Power System Data and Analytics -- 1.1 Introduction -- 1.2 Background -- 1.2.1 Concept, Opportunities, and Challenges of Present and Future Power Systems -- 1.2.2 Transformation in the Power Industry -- 1.2.3 Drivers and Barriers -- 1.3 Data‐rich Power Systems -- 1.3.1 Data Sources and Types -- 1.3.2 Data Structure -- 1.4 Data Analytics in Power Systems -- 1.4.1 What Is Data Analytics? -- 1.4.2 Analytics Techniques -- 1.5 Data Analytics‐Based Decision‐Making in Future Power Systems -- 1.5.1 Decision Framework -- 1.5.1.1 Uncertainty Issues -- 1.5.1.2 Behavioral Analytics -- 1.5.1.3 Policy Mechanisms -- 1.5.2 Computational Aspects -- 1.6 Conclusion -- 1.7 Future Trends and Challenges -- References -- Chapter 2 Advanced Predictive Modeling for Energy Consumption and Demand -- 2.1 The Role of Load Forecasting in Power System Planning -- 2.2 Need for Short‐Term Demand Forecasting -- 2.3 Components of Power Demand and Factors Affecting Demand Growth -- 2.3.1 Electricity Demand from the Consumer Type Perspective -- 2.3.2 Electricity Demand from the Supply Perspective -- 2.4 Electricity Demand in Networks with High Renewable Energy Sources -- 2.5 Machine Learning and Its Applications in Demand Forecast -- 2.5.1 Application of Clustering in Load Forecasting -- 2.6 The Impact of Macro‐decisions on Long‐term Load Forecasting -- 2.6.1 Natural Gas as a Primary Energy Carrier for Heating Demand -- 2.7 Conclusion -- References -- Chapter 3 Demand Response and Customer‐Centric Energy Management -- 3.1 Introduction -- 3.2 Background -- 3.3 Future Power Systems Aspects, Trends, and Challenges -- 3.4 Transforming to Customer‐Centric Era -- 3.4.1 Differences Between Customer‐Centric DR Solution and Other Ways in the Future Power System.
3.4.2 Drivers and Enablers -- 3.5 Customer‐Centric Power System Structure -- 3.5.1 Physical Layer -- 3.5.1.1 Physical Resources -- 3.5.1.2 Physical Constraints of the System -- 3.5.2 Cyber‐Social Layers -- 3.5.2.1 Centralized Approach (Traditional) -- 3.5.2.2 Decentralized Approach (Future) -- 3.6 Conclusion and Future Trends -- References -- Chapter 4 Applications of Data Mining in Industrial Tariff Design and Energy Management: Concepts and Practical Insights -- 4.1 Introduction -- 4.1.1 Data Mining: Concepts, Procedures, and Tools -- 4.1.2 Energy Management and the Role of Data Mining -- 4.1.3 Aims and Scope -- 4.2 Investigating Industrial Load Data: Analysis Through Various Indexes -- 4.3 Classification of Industries -- 4.4 Discussion and Conclusions -- References -- Chapter 5 Data‐Driven Tariff Design for Equitable Energy Distribution -- 5.1 Introduction -- 5.1.1 Literature Review and Contributions -- 5.1.2 Chapter Organization -- 5.2 Proposed Approach and Formulations -- 5.3 Describing the Case Study -- 5.4 Simulation Results -- 5.5 Conclusions and Future Works -- References -- Chapter 6 Applying Artificial Intelligence to Improve the Penetration of Renewable Energy in Power Systems -- 6.1 Introduction -- 6.2 Machine Learning Techniques -- 6.2.1 Artificial Neural Network and Deep Neural Network -- 6.2.2 Convolutional Neural Network -- 6.2.3 Recurrent Neural Network -- 6.2.4 Long Short‐Term Memory -- 6.3 General View of ML/DL Methods for RES Integration -- 6.3.1 Data Preprocessing -- 6.3.1.1 Normalization -- 6.3.1.2 Wrong/Missing Values and Outliers -- 6.3.1.3 Data Resolution -- 6.3.1.4 Inactive Time Data -- 6.3.1.5 Data Augmentation -- 6.3.1.6 Correlation -- 6.3.1.7 Data Clustering -- 6.3.2 Deterministic/Probabilistic Forecasting Methods -- 6.3.2.1 Deterministic Methods -- 6.3.2.2 Probabilistic Forecasting Methods -- 6.3.3 Evaluation Measures. 6.4 ML/DL Application for Integration of RES -- 6.4.1 Renewable Resources Data Prediction/Planning -- 6.4.2 RES Power Generation Prediction/Operation -- 6.4.3 Electric Load and Demand Forecasting -- 6.4.4 Stability Analysis -- 6.4.4.1 Security Assessment -- 6.4.4.2 Stability Assessment -- 6.5 Integrated Machine Learning and Optimization Approach -- 6.6 Conclusion -- References -- Chapter 7 Machine Learning‐Based Solutions for Renewable Energy Integration -- 7.1 Introduction -- 7.2 Machine Learning Importance in RESs Sector -- 7.2.1 AI‐Based Algorithms in RESs -- 7.2.2 ML Algorithms Application in RESs -- 7.3 Role of ML in Optimizing Renewable Energy Generation -- 7.3.1 Different Programming Models in RES Optimization -- 7.3.2 Optimization Objectives in RESs -- 7.3.3 ML Applications in Optimizing Renewable Energy Generation -- 7.4 Ensuring Grid Stability Through ML‐Based Forecasting -- 7.4.1 Grid Stability Forecasting -- 7.4.2 Grid Stability Through ML‐Based Forecasting -- 7.5 Challenges and Future Direction in ML‐Based Approaches to RESs -- 7.5.1 Challenges in ML‐Based Approaches to RESs -- 7.5.2 Future Directions in ML‐Based Approaches to RESs -- 7.6 Conclusion -- References -- Chapter 8 Application of Artificial Neural Networks in Solar Photovoltaic Power Forecasting -- 8.1 RES Share in World Energy Transition -- 8.2 Applications of PV Panels in Energy Systems -- 8.3 Disadvantages of PV Panels -- 8.4 Importance of PV Power Forecasting -- 8.5 Proposed Algorithm for PV Power Prediction -- 8.6 Numerical Results and Discussions -- 8.7 Concluding Remarks -- References -- Chapter 9 Power System Resilience Evaluation Data Challenges and Solutions -- 9.1 Introduction -- 9.2 A Review of Power System Resilience Metrics -- 9.3 The General Framework for the Resilience Assessment of the Power System -- 9.4 Data Required for Power System Resilience Studies. 9.4.1 Data of Natural Origin -- 9.4.2 Basic Data of the Power System -- 9.4.3 Data on Failure and Restoration Rates -- 9.5 Data Analysis and Correction -- 9.6 Disaster Forecasting in Power System Resilience Studies -- 9.7 Modeling the Impact of Disaster on Power System Performance -- 9.8 Static Model in Machine Learning -- 9.9 Spatiotemporal Random Process -- 9.9.1 Dynamic Model for Chain Failures -- 9.9.2 Nonstationary Failure‐Recovery‐Impact Processes -- 9.10 Lessons Learned and Concluding Remarks -- 9.11 Future Work -- References -- Chapter 10 Nonintrusive Load Monitoring in Smart Grids Using Deep Learning Approach -- 10.1 Introduction -- 10.2 Deep Learning Neural Networks -- 10.2.1 RNN -- 10.2.2 LSTM -- 10.2.3 CNN -- 10.2.4 Convolutional Layer -- 10.2.5 Pooling Layer -- 10.2.6 Fully Connected Layer -- 10.3 The Proposed Method -- 10.3.1 Pre‐Processing and Preparing Data -- 10.3.2 Proposed Method Architecture -- 10.3.3 Proposed Method's Parameters -- 10.3.4 Performance Evaluation -- 10.4 Results and Discussion -- 10.5 Challenges and Future Trends -- 10.6 Conclusion -- References -- Chapter 11 Power System Cyber‐Physical Security and Resiliency Based on Data‐Driven Methods -- 11.1 Introduction -- 11.2 Fundamental Concepts -- 11.2.1 Cyber‐Physical Power System (CPPS) -- 11.2.2 Security and Resiliency -- 11.3 Role of Data Analytics -- 11.3.1 Basic Methods -- 11.3.1.1 Supervised Learning (SL) -- 11.3.1.2 Unsupervised Learning (UL) -- 11.3.2 Advanced Techniques -- 11.3.2.1 Dimensionality Reduction (DR) -- 11.3.2.2 Feature Engineering -- 11.3.2.3 Reinforcement Learning -- 11.3.2.4 Integrated Models -- 11.4 Interdependency Modeling -- 11.4.1 Direct Modeling -- 11.4.2 Testbeds -- 11.4.3 Game‐Theoretic -- 11.4.4 Machine Learning -- 11.5 Cyber‐Physical Threats -- 11.5.1 Physical Attacks -- 11.5.2 Cyberattacks -- 11.5.2.1 Confidentiality. 11.5.2.2 Availability -- 11.5.2.3 Integrity -- 11.5.3 Coordinated Attacks -- 11.6 Defense Framework -- 11.6.1 Preventive Measures -- 11.6.1.1 Supply Chain Security -- 11.6.1.2 Access Control -- 11.6.1.3 Personnel Training -- 11.6.1.4 Resource Allocation -- 11.6.1.5 Infrastructure Hardening -- 11.6.1.6 Moving Target Defense -- 11.6.2 Mitigation Actions -- 11.6.2.1 Attack Detection -- 11.6.2.2 Data Recovery -- 11.6.2.3 Reconfiguration and Restoration -- 11.6.2.4 Forensic Analysis -- 11.7 Conclusion -- References -- Chapter 12 Application of Artificial Intelligence in Undervoltage Load Shedding in Digitalized Power Systems -- 12.1 Introduction -- 12.2 Load‐Shedding Strategies -- 12.2.1 Conventional LS -- 12.2.2 Adaptive LS -- 12.2.3 AI‐Based LS -- 12.3 Principles of UVLS -- 12.3.1 Amount of Load Shed -- 12.3.2 Location for LS -- 12.3.3 Application of VSI for UVLS -- 12.4 AI‐Based Methods -- 12.5 Case Study -- 12.5.1 Database Generation -- 12.5.2 Offline Training -- 12.5.3 Online Application -- 12.6 Future Challenges and Transfer Learning -- 12.7 Conclusion -- References -- Index -- EULA. |
| Record Nr. | UNINA-9911042410503321 |
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Arasteh Hamidreza
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| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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End use load profiles for the U.S. building stock / / Elaina Present
| End use load profiles for the U.S. building stock / / Elaina Present |
| Autore | Present Elaina |
| Pubbl/distr/stampa | Golden, CO : , : National Renewable Energy Laboratory, , 2019 |
| Descrizione fisica | 1 online resource (47 pages) : color illustrations |
| Collana | NREL/PR |
| Soggetto topico |
Energy consumption - Forecasting
Buildings - Energy conservation - United States |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | End use load profiles for the United States building stock |
| Record Nr. | UNINA-9910713886203321 |
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Present Elaina
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| Golden, CO : , : National Renewable Energy Laboratory, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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End-use load profiles for the U.S. building stock / / Elaina Present, Eric Wilson, Andrew Parker, Natalie Mims Frick
| End-use load profiles for the U.S. building stock / / Elaina Present, Eric Wilson, Andrew Parker, Natalie Mims Frick |
| Autore | Present Elaina |
| Pubbl/distr/stampa | Golden, CO : , : National Renewable Energy Laboratory, , 2020 |
| Descrizione fisica | 1 online resource (1 page) : color illustrations |
| Collana | NREL/PO |
| Soggetto topico |
Energy consumption - Forecasting
Buildings - Energy conservation - United States |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910713949503321 |
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Present Elaina
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| Golden, CO : , : National Renewable Energy Laboratory, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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End-use load profiles for the U.S. building stock : market needs, use cases, and data gaps / / Natalie Mims Frick [and 8 others]
| End-use load profiles for the U.S. building stock : market needs, use cases, and data gaps / / Natalie Mims Frick [and 8 others] |
| Pubbl/distr/stampa | [Washington, D.C.] : , : United States Department of Energy, Office of Energy Efficiency and Renewable Energy, , 2019 |
| Descrizione fisica | 1 online resource (xv, 50 pages) : color illustrations |
| Collana | NREL/TP |
| Soggetto topico |
Energy consumption - Forecasting
Buildings - Energy conservation - United States Buildings - Energy conservation |
| Soggetto genere / forma | Technical reports. |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | End-use load profiles for the U.S. building stock |
| Record Nr. | UNINA-9910713835303321 |
| [Washington, D.C.] : , : United States Department of Energy, Office of Energy Efficiency and Renewable Energy, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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