In recent power grids, the need for having a two-way flow of information and electricity is crucial. This provides the opportunity for suppliers and customers to better communicate with each other by shifting traditional power grids to smart grids (SGs). In this paper, demand response management (DRM) is investigated as it plays an important role in SGs to prevent blackouts and provide economic and environmental benefits for both end-users and energy providers. In modern power grids, the development of communication networks has enhanced DRM programmes and made the grid smarter. In particular, with progresses in the 5G Internet of Things (IoT), the infrastructure for DRM programmes is improved with fast data transfer, higher reliability, increased security, lower power consumption, and a massive number of connections. Therefore, this paper provides a comprehensive review of potential applications of 5G IoT technologies as well as the computational and analytical algorithms applied for DRM programmes in SGs. The review holistically brings together sensing, communication, and computing (optimization, prediction), areas usually studied in a scattered way. A broad discussion on various DRM programmes in different layers of enhanced 5G IoT based SGs is given, paying particular attention to advances in machine learning (ML) and deep learning (DL) algorithms alongside challenges in security, reliability, and other factors that have a role in SGs' performance.INDEX TERMS Smart grid, demand response management, 5G , Internet of Things
Short-term forecasting of heat demand is crucial for controlling district heating networks and integrated electricity and heat supply systems. The forecast specifies an estimate of the energy required in the coming hours which enables the controller to proactively manage the storage units and schedule the heat generation. Consequently, improving the accuracy of heat demand forecasting can lead to reduced operational cost and increased reliability of the energy supply. This paper presents the development of a sample weighted Support Vector Machine (SVM) to improve the accuracy of heating demand forecasting. As the dynamics of heat demand time series change over time, recurrence plot analysis is first used to investigate any seasonal behavior and its relationship to ambient temperature. Then, to capture this seasonal behavior, a membership-function-based method is presented to generate the weight of each sample in learning a SVM model. This method is evaluated using a dataset with half hourly resolution from an industrial case study in the UK. Compared to conventional forecasting methods, the proposed approach shows significantly better accuracy in 24 hours ahead forecasting of heat demand.
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