{"title":"Engenharia De Energia","description":null,"products":[{"product_id":"hybrid-advanced-optimization-methods-with-evolutionary-computation-techniques-in-energy-forecasting","title":"Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting","description":"\u003cp\u003eMore accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers.\u003c\/p\u003e\u003cp\u003eThis book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy.\u003c\/p\u003e","brand":"MDPI AG","offers":[{"title":"Default Title","offer_id":67805769662831,"sku":"9783038972860","price":401.38,"currency_code":"BRL","in_stock":true}]},{"product_id":"hybrid-advanced-techniques-for-forecasting-in-energy-sector","title":"Hybrid Advanced Techniques for Forecasting in Energy Sector","description":"\u003cp\u003eAccurate 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), etc.). These advanced novel hybrid techniques can provide more satisfactory energy forecasting performances.\u003c\/p\u003e\u003cp\u003eThis book aimed to attract researchers with an interest in the research areas described above. Specifically, we were interested in contributions towards recent developments, i.e., 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.\u003c\/p\u003e","brand":"MDPI AG","offers":[{"title":"Default Title","offer_id":67805769761135,"sku":"9783038972907","price":401.38,"currency_code":"BRL","in_stock":true}]}],"url":"https:\/\/internacional.umlivro.com.br\/collections\/engenharia-de-energia.oembed","provider":"UmLivro Internacional","version":"1.0","type":"link"}