LEADER 02494nam 2200337 450 001 9910674006603321 005 20230629124715.0 035 $a(CKB)4100000011302213 035 $a(NjHacI)994100000011302213 035 $a(EXLCZ)994100000011302213 100 $a20230629d2020 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIntelligent Optimization Modelling in Energy Forecasting /$fWei-Chiang Hong 210 1$aBasel :$cMDPI - Multidisciplinary Digital Publishing Institute,$d2020. 215 $a1 online resource (262 pages) 311 $a3-03928-364-2 330 $aAccurate energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in power system operation and security, economic energy use, contingency scheduling, the planning and maintenance of energy supply systems, and so on. In recent decades, many energy forecasting models have been continuously proposed to improve forecasting accuracy, including traditional statistical models (e.g., ARIMA, SARIMA, ARMAX, multi-variate regression, exponential smoothing models, Kalman filtering, Bayesian estimation models, etc.) and artificial intelligence models (e.g., artificial neural networks (ANNs), knowledge-based expert systems, evolutionary computation models, support vector regression, etc.). Recently, due to the great development of optimization modeling methods (e.g., quadratic programming method, differential empirical mode method, evolutionary algorithms, meta-heuristic algorithms, etc.) and intelligent computing mechanisms (e.g., quantum computing, chaotic mapping, cloud mapping, seasonal mechanism, etc.), many novel hybrid models or models combined with the above-mentioned intelligent-optimization-based models have also been proposed to achieve satisfactory forecasting accuracy levels. It is important to explore the tendency and development of intelligent-optimization-based modeling methodologies and to enrich their practical performances, particularly for marine renewable energy forecasting. 606 $aInformation technology 615 0$aInformation technology. 676 $a004 700 $aHong$b Wei-Chiang$0913784 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9910674006603321 996 $aIntelligent Optimization Modelling in Energy Forecasting$93391304 997 $aUNINA