Hybrid Advanced Techniques For Forecasting In Energy Sector
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Hybrid Advanced Techniques for Forecasting in Energy Sector
Author | : Wei-Chiang Hong |
Publsiher | : MDPI |
Total Pages | : 251 |
Release | : 2018-10-19 |
Genre | : Electronic books |
ISBN | : 9783038972907 |
Download Hybrid Advanced Techniques for Forecasting in Energy Sector Book in PDF, Epub and Kindle
This book is a printed edition of the Special Issue "Hybrid Advanced Techniques for Forecasting in Energy Sector" that was published in Energies
Hybrid Advanced Techniques for Forecasting in Energy Sector
Author | : Wei-Chiang Hong |
Publsiher | : Unknown |
Total Pages | : 135 |
Release | : 2018 |
Genre | : Electronic Book |
ISBN | : 3038972916 |
Download Hybrid Advanced Techniques for Forecasting in Energy Sector Book in PDF, Epub and Kindle
Accurate forecasting performance in the energy sector is a primary factor in the modern restructured power market, accomplished by any novel advanced hybrid techniques. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated by factors such as seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. To comprehensively address this issue, it is insufficient to concentrate only on simply hybridizing evolutionary algorithms with each other, or on hybridizing evolutionary algorithms with chaotic mapping, quantum computing, recurrent and seasonal mechanisms, and fuzzy inference theory in order to determine suitable parameters for an existing model. It is necessary to also consider hybridizing or combining two or more existing models (e.g., neuro-fuzzy model, BPNN-fuzzy model, seasonal support vector regression-chaotic quantum particle swarm optimization (SSVR-CQPSO), et cetera). These advanced novel hybrid techniques can provide more satisfactory energy forecasting performances. This book aimed to attract researchers with an interest in the research areas described above. Specifically, we were interested in contributions towards recent developments, id est, hybridizing or combining any advanced techniques in energy forecasting, with the superior capabilities over the traditional forecasting approaches, with the ability to overcome some embedded drawbacks, and with the very superiority to achieve significant improved forecasting accuracy.
Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting
Author | : Wei-Chiang Hong |
Publsiher | : MDPI |
Total Pages | : 251 |
Release | : 2018-10-19 |
Genre | : Electronic books |
ISBN | : 9783038972860 |
Download Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting Book in PDF, Epub and Kindle
This book is a printed edition of the Special Issue "Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting" that was published in Energies
Short Term Load Forecasting by Artificial Intelligent Technologies
Author | : Wei-Chiang Hong,Ming-Wei Li,Guo-Feng Fan |
Publsiher | : MDPI |
Total Pages | : 445 |
Release | : 2019-01-29 |
Genre | : Electronic Book |
ISBN | : 9783038975823 |
Download Short Term Load Forecasting by Artificial Intelligent Technologies Book in PDF, Epub and Kindle
This book is a printed edition of the Special Issue "Short-Term Load Forecasting by Artificial Intelligent Technologies" that was published in Energies
Forecasting Models of Electricity Prices
Author | : Javier Contreras |
Publsiher | : MDPI |
Total Pages | : 259 |
Release | : 2018-04-06 |
Genre | : Electronic Book |
ISBN | : 9783038424154 |
Download Forecasting Models of Electricity Prices Book in PDF, Epub and Kindle
This book is a printed edition of the Special Issue "Forecasting Models of Electricity Prices" that was published in Energies
Advanced Statistical Modeling Forecasting and Fault Detection in Renewable Energy Systems
Author | : Fouzi Harrou,Ying Sun |
Publsiher | : BoD – Books on Demand |
Total Pages | : 212 |
Release | : 2020-04-01 |
Genre | : Technology & Engineering |
ISBN | : 9781838800918 |
Download Advanced Statistical Modeling Forecasting and Fault Detection in Renewable Energy Systems Book in PDF, Epub and Kindle
Fault detection, control, and forecasting have a vital role in renewable energy systems (Photovoltaics (PV) and wind turbines (WTs)) to improve their productivity, ef?ciency, and safety, and to avoid expensive maintenance. For instance, the main crucial and challenging issue in solar and wind energy production is the volatility of intermittent power generation due mainly to weather conditions. This fact usually limits the integration of PV systems and WTs into the power grid. Hence, accurately forecasting power generation in PV and WTs is of great importance for daily/hourly efficient management of power grid production, delivery, and storage, as well as for decision-making on the energy market. Also, accurate and prompt fault detection and diagnosis strategies are required to improve efficiencies of renewable energy systems, avoid the high cost of maintenance, and reduce risks of fire hazards, which could affect both personnel and installed equipment. This book intends to provide the reader with advanced statistical modeling, forecasting, and fault detection techniques in renewable energy systems.
Prediction Techniques for Renewable Energy Generation and Load Demand Forecasting
Author | : Anuradha Tomar,Prerna Gaur,Xiaolong Jin |
Publsiher | : Springer Nature |
Total Pages | : 208 |
Release | : 2023-01-20 |
Genre | : Technology & Engineering |
ISBN | : 9789811964909 |
Download Prediction Techniques for Renewable Energy Generation and Load Demand Forecasting Book in PDF, Epub and Kindle
This book provides an introduction to forecasting methods for renewable energy sources integrated with existing grid. It consists of two sections; the first one is on the generation side forecasting methods, while the second section deals with the different ways of load forecasting. It broadly includes artificial intelligence, machine learning, hybrid techniques and other state-of-the-art techniques for renewable energy and load predictions. The book reflects the state of the art in distributed generation system and future microgrids and covers theory, algorithms, simulations and case studies. It offers invaluable insights through this valuable resource to students and researchers working in the fields of renewable energy, integration of renewable energy with existing grid and electrical distribution network.
Recent Advances in Renewable Energy Automation and Energy Forecasting
Author | : Sarat Kumar Sahoo,Franco Fernando Yanine,Vikram Kulkarni,Akhtar Kalam |
Publsiher | : Frontiers Media SA |
Total Pages | : 196 |
Release | : 2023-12-08 |
Genre | : Technology & Engineering |
ISBN | : 9782832541678 |
Download Recent Advances in Renewable Energy Automation and Energy Forecasting Book in PDF, Epub and Kindle
The advancement of sustainable energy is becoming an important concern for many countries. The traditional electrical grid supports only one-way interaction of power being delivered to the consumers. The emergence of improved sensors, actuators, and automation technologies has consequently improved the control, monitoring and communication techniques within the energy sector, including the Smart Grid system. With the support of the aforementioned modern technologies, the information flows in two-ways between the consumer and supplier. This data communication helps the supplier in overcoming challenges like integration of renewable technologies, management of energy demand, load automation and control. Renewable energy (RE) is intermittent in nature and therefore difficult to predict. The accurate RE forecasting is very essential to improve the power system operations. The forecasting models are based on complex function combinations that include seasonality, fluctuation, and dynamic nonlinearity. The advanced intelligent computing algorithms for forecasting should consider the proper parameter determinations for achieving optimization. For this we need, new generation research areas like Machine learning (ML), and Artificial Intelligence (AI) to enable the efficient integration of distributed and renewable generation at large scale and at all voltage levels. The modern research in the above areas will improve the efficiency, reliability and sustainability in the Smart grid.