A review of datasets and load forecasting techniques for smart natural gas and water grids: Analysis and experiments

Neurocomputing

Volumn 170, Issue , 2015, Pages 448-465

  Fagiani, M ^a   Squartini, S ^a   Gabrielli, L ^a   Spinsante, S ^a   Piazza, F ^a  

^a UNIVERSITÀ POLITECNICA DELLE MARCHE   (Italy)

Author keywords

Computational intelligence; Forecasting techniques; Heterogeneous data forecasting; Machine learning; Short long term load forecasting; Smart water gas grid

Indexed keywords

ARTIFICIAL INTELLIGENCE; DEEP LEARNING; ELECTRIC POWER PLANT LOADS; EXTENDED KALMAN FILTERS; GASES; GENETIC ALGORITHMS; GENETIC PROGRAMMING; INTELLIGENT COMPUTING; LEARNING SYSTEMS; NATURAL GAS; SUPPORT VECTOR REGRESSION;

ENERGY INFORMATION ADMINISTRATION; FORECASTING TECHNIQUES; HETEROGENEOUS DATA; LOAD FORECASTING; MACHINE LEARNING TECHNIQUES; SMART WATER/GAS GRID; STATE-OF-THE ART REVIEWS; TEMPERATURE INFORMATION;

FORECASTING;

NATURAL GAS; WATER;

ADAPTATION; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; DATA BASE; DEEP BELIEF NETWORK; ECHO STATE NETWORK; ENERGY; ENERGY EXPENDITURE; EVOLUTIONARY ALGORITHM; FEEDBACK SYSTEM; FLUID INTAKE; FORECASTING; GENETIC REPROGRAMMING; HUMIDITY; INFORMATION PROCESSING; INTELLIGENCE; IRAN; MACHINE LEARNING; MEDICAL RESEARCH; NUCLEAR REPROGRAMMING; PREDICTION; PRIORITY JOURNAL; PROCESS OPTIMIZATION; REGRESSION ANALYSIS; REPRODUCIBILITY; SENSITIVITY ANALYSIS; STOCHASTIC MODEL; SUPPORT VECTOR MACHINE; TEMPERATURE;

EID: 84940614413     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2015.04.098     Document Type: Article

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