TY  - GEN
AU  - Gómez Vela,Francisco A.
AU  - García-Torres,Miguel
AU  - Divina,Federico
AU  - Gómez Vela,Francisco A.
AU  - García-Torres,Miguel
AU  - Divina,Federico
TI  - Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast
SN  - books978-3-0365-0863-4
PY  - 2021///
CY  - Basel, Switzerland
PB  - MDPI - Multidisciplinary Digital Publishing Institute
KW  - Research & information: general
KW  - bicssc
KW  - Technology: general issues
KW  - deep learning
KW  - energy demand
KW  - temporal convolutional network
KW  - time series forecasting
KW  - time series
KW  - forecasting
KW  - exponential smoothing
KW  - electricity demand
KW  - residential building
KW  - energy efficiency
KW  - clustering
KW  - decision tree
KW  - time-series forecasting
KW  - evolutionary computation
KW  - neuroevolution
KW  - photovoltaic power plant
KW  - short-term forecasting
KW  - data processing
KW  - data filtration
KW  - k-nearest neighbors
KW  - regression
KW  - autoregression
KW  - n/a
N1  - Open Access
N2  - The 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
UR  - https://mdpi.com/books/pdfview/book/3931
UR  - https://directory.doabooks.org/handle/20.500.12854/76485
ER  -