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Heat networks play a vital role in the energy sector by offering thermal energy to residents in certain countries. Effective management and optimization of heat networks require a deep understanding of users' heat usage patterns. Irregular patterns, such as peak usage periods, can exceed the design capacities of the system. However, previous work has mostly neglected the analysis of heat usage profiles or performed on a small scale. To close the gap, this study proposes a data-driven approach to analyze and predict heat load in a district heating network. The study uses data from over eight heating seasons of a cogeneration DH plant in Cheongju, Korea, to build analysis and forecast models using supervised machine learning (ML) algorithms, including support vector regression (SVR), boosting algorithms, and multilayer perceptron (MLP). The models take weather data, holiday information, and historical hourly heat load as input variables. The performance of these algorithms is compared using different training sample sizes of the dataset. The results show that boosting algorithms, particularly XGBoost, are more suitable ML algorithms with lower prediction errors than SVR and MLP. Finally, different explainable artificial intelligence approaches are applied to provide an in-depth interpretation of the trained model and the importance of input variables. [ABSTRACT FROM AUTHOR]

Heat usage patterns, which are greatly affected by the users' behaviors, network performances, and control logic, are a crucial indicator of the effective and efficient management of district heating networks. The variations in the heat load can be daily or seasonal. The daily variations are primarily influenced by the customers' social behaviors, whereas the seasonal variations are mainly caused by the large temperature differences between the seasons over the year. Irregular heat load patterns can significantly raise costs due to pricey peak fuels and increased peak heat load capacities. The in-depth analyses of heat load profiles are regrettably quite rare and small-scale up until now. Therefore, this study offers a comprehensive investigation of a district heating network operation in order to exploit the major features of the heat usage patterns and discover the big factors that affect the heat load patterns. In addition, this study also provides detailed explanations of the features that can be considered the main drivers of the users' heat load demand. Finally, two primary daily heat usage patterns are extracted, which are exploited to efficiently train the prediction model. [ABSTRACT FROM AUTHOR]

Heat usage patterns, which are greatly affected by the users' behaviors, network performances, and control logic, are a crucial indicator of the effective and efficient management of district heating networks. The variations in the heat load can be daily or seasonal. The daily variations are primarily influenced by the customers' social behaviors, whereas the seasonal variations are mainly caused by the large temperature differences between the seasons over the year. Irregular heat load patterns can significantly raise costs due to pricey peak fuels and increased peak heat load capacities. The in-depth analyses of heat load profiles are regrettably quite rare and small-scale up until now. Therefore, this study offers a comprehensive investigation of a district heating network operation in order to exploit the major features of the heat usage patterns and discover the big factors that affect the heat load patterns. In addition, this study also provides detailed explanations of the features that can be considered the main drivers of the users' heat load demand. Finally, two primary daily heat usage patterns are extracted, which are exploited to efficiently train the prediction model. [ABSTRACT FROM AUTHOR]

This paper proposes the Internet connectivity of RF-powered devices in the backscatter system. The RF-powered devices do not use a battery and charge energy by harvesting from ambient RF signals of TV, a cellular phone and Wi-Fi devices. The Internet connectivity of the RF-powered devices in the backscatter system is very useful in Internet of things technology because the RF-powered devices which are called to a tag have a small size by the harvesting from ambient RF signals without a battery. This paper proposes a method improving the communication performance of the Wi-Fi backscatter system by applying the cooperative communication scheme. [ABSTRACT FROM AUTHOR]

These authors contributed equally: L. Minh Dang and Sujin Lee. Correction to: I Scientific Reports i https://doi.org/10.1038/s41598-022-13030-6, published online 02 June 2022 In the original version of this Article, Sujin Lee was incorrectly affiliated with 'Department of Computer Science and Engineering, Sejong University, Seoul, Republic of Korea'. [Extracted from the article]