Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast
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...
محفوظ في:
| التنسيق: | Online |
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| اللغة: | الإنجليزية |
| منشور في: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
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| الموضوعات: | |
| الوصول للمادة أونلاين: | ONIX_20220111_9783036508627_221 |
| الوسوم: |
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| _version_ | 1869518923939971072 |
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| collection | Directory of Open Access Books |
| description | 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 |
| format | Online |
| id | doab-20.500.12854ir-76485 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-764852024-03-28T03:31:38Z Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast Gómez Vela, Francisco A. García-Torres, Miguel Divina, Federico 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 thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues 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 2022-01-11T13:33:16Z 2022-01-11T13:33:16Z 2021 book ONIX_20220111_9783036508627_221 9783036508627 9783036508634 https://directory.doabooks.org/handle/20.500.12854/76485 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/3931 https://mdpi.com/books/pdfview/book/3931 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-0863-4 10.3390/books978-3-0365-0863-4 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036508627 9783036508634 100 Basel, Switzerland open access |
| spellingShingle | 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 thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast |
| title | Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast |
| title_full | Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast |
| title_fullStr | Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast |
| title_full_unstemmed | Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast |
| title_short | Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast |
| title_sort | advanced optimization methods and big data applications in energy demand forecast |
| topic | 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 thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues |
| topic_facet | 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 thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues |
| url | ONIX_20220111_9783036508627_221 |