Abstract
Green energy management is an economical solution for better energy usage, but the employed literature lacks focusing on the potentials of edge intelligence in controllable Internet of Things (IoT). Therefore, in this article, we focus on the requirements of todays' smart grids, homes, and industries to propose a deep-learning-based framework for intelligent energy management. We predict future energy consumption for short intervals of time as well as provide an efficient way of communication between energy distributors and consumers. The key contributions include edge devices-based real-time energy management via common cloud-based data supervising server, optimal normalization technique selection, and a novel sequence learning-based energy forecasting mechanism with reduced time complexity and lowest error rates. In the proposed framework, edge devices relate to a common cloud server in an IoT network that communicates with the associated smart grids to effectively continue the energy demand and response phenomenon. We apply several preprocessing techniques to deal with the diverse nature of electricity data, followed by an efficient decision-making algorithm for short-term forecasting and implement it over resource-constrained devices. We perform extensive experiments and witness 0.15 and 3.77 units reduced mean-square error (MSE) and root MSE (RMSE) for residential and commercial datasets, respectively.
Highlights
E NERGY management at smart grids via automated techniques for future load forecasting is an interesting area of Manuscript received March 25, 2020; revised June 15, 2020; accepted July 11, 2020
Another research [29] presented a hybrid technique for energy forecasting of residential buildings, where they incorporated deep learning and genetic algorithms with long short-term memory (LSTM) to propose an optimized objective function with hidden neurons for energy forecasting
The energy management tier is entirely responsible for energy consumption prediction and its appropriate management, where a cloud server is involved as a third-party communicator between consumers and smart grids
Summary
E NERGY management at smart grids via automated techniques for future load forecasting is an interesting area of Manuscript received March 25, 2020; revised June 15, 2020; accepted July 11, 2020. The cloud [11] and fog computing [12], [13] paradigms are scarcely utilized in the energy forecasting literature, which are trustworthy platforms for efficient big data analysis and instant decision making, such as anomalous energy demand prediction To handle these issues efficiently and effectively in controllable IoT networks by using deep learning strategies, we propose a novel edgeintelligence-based energy forecasting framework for smart grids energy management with the following contributions. 2) We present an infrastructure to deploy resourceconstrained controllable devices at variable consumer locations (smart homes or industries), that are connected through IoT network with cloud supervising server to upload their current demands and inform about the future requirements.
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