Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast
Gómez Vela, Francisco A.
Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 - 1 electronic resource (100 p.)
Open Access
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
Creative Commons
English
books978-3-0365-0863-4 9783036508627 9783036508634
10.3390/books978-3-0365-0863-4 doi
Research & information: general
Technology: general issues
deep learning energy demand temporal convolutional network time series forecasting time series forecasting exponential smoothing electricity demand residential building energy efficiency clustering decision tree time-series forecasting evolutionary computation neuroevolution photovoltaic power plant short-term forecasting data processing data filtration k-nearest neighbors regression autoregression n/a
Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 - 1 electronic resource (100 p.)
Open Access
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
Creative Commons
English
books978-3-0365-0863-4 9783036508627 9783036508634
10.3390/books978-3-0365-0863-4 doi
Research & information: general
Technology: general issues
deep learning energy demand temporal convolutional network time series forecasting time series forecasting exponential smoothing electricity demand residential building energy efficiency clustering decision tree time-series forecasting evolutionary computation neuroevolution photovoltaic power plant short-term forecasting data processing data filtration k-nearest neighbors regression autoregression n/a