FastInformer-HEMS: A Lightweight Optimization Algorithm for Home Energy Management Systems
Xihui Chen,Dejun Ning +1 more
TL;DR: In this paper , the authors proposed a lightweight optimization algorithm called FastInformer-HEMS, which introduces the E-Attn attention mechanism based on Informer and uses global average pooling to extract the attention characteristics.
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Abstract: In a smart home with distributed energy resources, the home energy management system (HEMS) controls the photovoltaic (PV) storage system by executing the optimization algorithm to achieve the lowest power cost. The existing mixed integer linear programming (MILP) algorithm is not suitable for execution on the end-user side due to its high computational complexity. The HEMS algorithm based on a long short-term memory neural network (LSTM-HEMS) can effectively solve the problem of the high computational complexity of MILP, but its optimization outcome is not high due to the accumulation of prediction errors. In order to achieve a better balance between computational complexity and optimization outcome, this paper proposes a lightweight optimization algorithm called the FastInformer-HEMS, which introduces the E-Attn attention mechanism based on Informer and uses global average pooling to extract the attention characteristics. Meanwhile, the proposed method introduces the maximum self-consumption algorithm as a backup strategy to ensure the safe operation of the system. The simulated results show that the computational complexity of the proposed FastInformer-HEMS is the lowest among the existing algorithms. Compared with the existing LSTM-HEMS, the proposed algorithm reduces the power consumption cost by 12.3% and 6.6% in the two typical scenarios, while the execution time decreases by 13.6 times.
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Citations
LSTM Networks for Home Energy Efficiency
Zurisaddai Severiche-Maury,Wilson Arrubla-Hoyos,Raúl Ramírez-Velarde,Dora Cama-Pinto,Juan A. Holgado-Terriza,Miguel Damas,Alejandro Cama-Pinto +6 more
TL;DR: This study develops and evaluates an LSTM neural network for predicting household energy consumption, achieving a mean squared error of 0.0169, and highlights the potential of LSTM models in smart-home energy management and future research directions.
1
Proposal of a Decision-Making Model for Home Energy Saving through Artificial Intelligence applied to a HEMS
Zurisaddai Seveiche-Maury,Wilson Arrubla-Hoyos +1 more
- 22 Nov 2023
TL;DR: An innovative model for efficient home energy management through a Home Energy Management System (HEMS) based on Deep Learning and Reinforcement Learning shows significant potential for optimizing household energy consumption, improving energy efficiency and promoting more sustainable practices.
1
Grey wolf optimization-based design of a miniaturized Gysel power divider with enhanced isolation for IoT and secure wireless systems
Seyed Abed Zonouri,Saeed Mehdipourbashi,Seyed Abed Zonouri,Saeed Mehdipourbashi +3 more
TL;DR: This study presents a miniaturized Gysel power divider optimized using Grey Wolf Optimization, achieving high isolation, low insertion loss, and strong harmonic suppression for IoT and secure wireless systems, outperforming PSO and GA in convergence speed and RF metrics.
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