01075nam0 22002653i 450 VAN008570520140728121704.52820111019d1995 |0itac50 baitaIT|||| |||||ˆIl ‰recupero della memoriaper una storia della Resistenza in Terra di Lavoro, autunno 1943Giuseppe Capobiancoprefazione di Guido D'AgostinoNapoli : Edizioni scientifiche italiane1995X232 p. : ill. ; 23 cmFondo F. M. d'Ippolito.NapoliVANL000005CapobiancoGiuseppeVANV054162252356D'AgostinoGuidoVANV017837ESI <editore>VANV108662650ITSOL20240329RICABIBLIOTECA DEL DIPARTIMENTO DI GIURISPRUDENZAIT-CE0105VAN00VAN0085705BIBLIOTECA DEL DIPARTIMENTO DI GIURISPRUDENZA00CONS FDI.XXIV.3 00FDI2489 20111019 Recupero della memoria634016UNICAMPANIA03123nam 22005053a 450 991034683840332120250203235433.09783038975830303897583410.3390/books978-3-03897-583-0(CKB)4920000000095256(oapen)https://directory.doabooks.org/handle/20.500.12854/59327(ScCtBLL)5963d334-6452-45bf-8fba-33b092dc5c6b(OCoLC)1163852235(EXLCZ)99492000000009525620250203i20192019 uu engurmn|---annantxtrdacontentcrdamediacrrdacarrierShort-Term Load Forecasting by Artificial Intelligent TechnologiesGuo-Feng Fan, Ming-Wei Li, Wei-Chiang HongBasel, Switzerland :MDPI,2019.1 electronic resource (444 p.)9783038975823 3038975826 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.meta-heuristic algorithmsartificial neural networks (ANNs)knowledge-based expert systemsstatistical forecasting modelsevolutionary algorithmsshort term load forecastingnovel intelligent technologiessupport vector regression/support vector machinesseasonal mechanismFan Guo-Feng1786239Li Ming-WeiHong Wei-ChiangScCtBLLScCtBLLBOOK9910346838403321Short-Term Load Forecasting by Artificial Intelligent Technologies4317657UNINA