LEADER 03413nam 2200661z- 450 001 9910557776003321 005 20231214133414.0 035 $a(CKB)5400000000045629 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76485 035 $a(EXLCZ)995400000000045629 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Optimization Methods and Big Data Applications in Energy Demand Forecast 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 electronic resource (100 p.) 311 $a3-0365-0862-7 311 $a3-0365-0863-5 330 $aThe use of data collectors in energy systems is growing more and more. For example, smart sensors are now widely used in energy production and energy consumption systems. This implies that huge amounts of data are generated and need to be analyzed in order to extract useful insights from them. Such big data give rise to a number of opportunities and challenges for informed decision making. In recent years, researchers have been working very actively in order to come up with effective and powerful techniques in order to deal with the huge amount of data available. Such approaches can be used in the context of energy production and consumption considering the amount of data produced by all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing, or data visualization are being successfully applied to energy demand forecasting. In light of the above, this Special Issue collects the latest research on relevant topics, in particular in energy demand forecasts, and the use of advanced optimization methods and big data techniques. Here, by energy, we mean any kind of energy, e.g., electrical, solar, microwave, or wind 606 $aResearch & information: general$2bicssc 606 $aTechnology: general issues$2bicssc 610 $adeep learning 610 $aenergy demand 610 $atemporal convolutional network 610 $atime series forecasting 610 $atime series 610 $aforecasting 610 $aexponential smoothing 610 $aelectricity demand 610 $aresidential building 610 $aenergy efficiency 610 $aclustering 610 $adecision tree 610 $atime-series forecasting 610 $aevolutionary computation 610 $aneuroevolution 610 $aphotovoltaic power plant 610 $ashort-term forecasting 610 $adata processing 610 $adata filtration 610 $ak-nearest neighbors 610 $aregression 610 $aautoregression 615 7$aResearch & information: general 615 7$aTechnology: general issues 700 $aGo?mez Vela$b Francisco A$4edt$00 702 $aGarci?a-Torres$b Miguel$4edt 702 $aDivina$b Federico$4edt 702 $aGo?mez Vela$b Francisco A$4oth 702 $aGarci?a-Torres$b Miguel$4oth 702 $aDivina$b Federico$4oth 906 $aBOOK 912 $a9910557776003321 996 $aAdvanced Optimization Methods and Big Data Applications in Energy Demand Forecast$93029690 997 $aUNINA