Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast

• Other Authors: Gómez Vela, Francisco A. (Editor), García-Torres, Miguel (Editor), Divina, Federico (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
• Subjects: 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
• Online Access: DOAB: download the publication; DOAB: description of the publication

Summary

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

Physical Description

1 electronic resource (100 p.)

ISBN

books978-3-0365-0863-4
9783036508627
9783036508634

Access

Open Access