LEADER 03123nam 22005053a 450 001 9910346838403321 005 20250203235433.0 010 $a9783038975830 010 $a3038975834 024 8 $a10.3390/books978-3-03897-583-0 035 $a(CKB)4920000000095256 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/59327 035 $a(ScCtBLL)5963d334-6452-45bf-8fba-33b092dc5c6b 035 $a(OCoLC)1163852235 035 $a(EXLCZ)994920000000095256 100 $a20250203i20192019 uu 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aShort-Term Load Forecasting by Artificial Intelligent Technologies$fGuo-Feng Fan, Ming-Wei Li, Wei-Chiang Hong 210 1$aBasel, Switzerland :$cMDPI,$d2019. 215 $a1 electronic resource (444 p.) 311 08$a9783038975823 311 08$a3038975826 330 $a In last few decades, short-term load forecasting (STLF) has been one of the most important research issues for achieving higher efficiency and reliability in power system operation, to facilitate the minimization of its operation cost by providing accurate input to day-ahead scheduling, contingency analysis, load flow analysis, planning, and maintenance of power systems. There are lots of forecasting models proposed for STLF, including traditional statistical models (such as ARIMA, SARIMA, ARMAX, multi-variate regression, Kalman filter, exponential smoothing, and so on) and artificial-intelligence-based models (such as artificial neural networks (ANNs), knowledge-based expert systems, fuzzy theory and fuzzy inference systems, evolutionary computation models, support vector regression, and so on). Recently, due to the great development of evolutionary algorithms (EA) and novel computing concepts (e.g., quantum computing concepts, chaotic mapping functions, and cloud mapping process, and so on), many advanced hybrids with those artificial-intelligence-based models are also proposed to achieve satisfactory forecasting accuracy levels. In addition, combining some superior mechanisms with an existing model could empower that model to solve problems it could not deal with before; for example, the seasonal mechanism from the ARIMA model is a good component to be combined with any forecasting models to help them to deal with seasonal problems. 610 $ameta-heuristic algorithms 610 $aartificial neural networks (ANNs) 610 $aknowledge-based expert systems 610 $astatistical forecasting models 610 $aevolutionary algorithms 610 $ashort term load forecasting 610 $anovel intelligent technologies 610 $asupport vector regression/support vector machines 610 $aseasonal mechanism 700 $aFan$b Guo-Feng$01786239 702 $aLi$b Ming-Wei 702 $aHong$b Wei-Chiang 801 0$bScCtBLL 801 1$bScCtBLL 906 $aBOOK 912 $a9910346838403321 996 $aShort-Term Load Forecasting by Artificial Intelligent Technologies$94317657 997 $aUNINA