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Integrated Architecture for Smart Grid Energy Management: Deep Attention-Enhanced Sequence-to-Sequence Model with Energy-Aware Optimized Reinforcement Learning for Demand Response

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Deepa K R at Reva University
Deepa K R
N. Thillaiarasu
N. Thillaiarasu
N. Thillaiarasu
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Abstract and Figures

Demand Response (DR) has become a key strategy for enhancing energy system sustainability and reducing costs. Deep Learning (DL) has emerged as crucial for managing DR's complexity and large data volumes, enabling near real-time decision-making. DL techniques can effectively tackle challenges such as selecting responsive users, understanding consumption behaviours, optimizing pricing, monitoring and controlling devices, engaging more consumers in DR schemes, and determining fair remuneration for participants. This research work presents an integrated architecture for smart grid energy management, combining a Deep Attention-Enhanced Sequence-to-Sequence Model (AES2S) with Energy-Aware Optimized Reinforcement Learning (EAORL). The objective is to design a system that performs non-intrusive load monitoring and optimizes demand response to enhance energy efficiency while maintaining user comfort. The AES2S module accurately performs appliance state identification and load disaggregation using convolutional layers, Enhanced Sequence-to-Sequence Model networks, and an attention mechanism. The EAORL module employs a multi-agent system, where each agent uses a Deep Q-Learning Network to learn optimal policies for adjusting energy consumption in response to grid conditions and user demand. The system uses an Iterative Policy Update mechanism, where agents update their policies sequentially, ensuring stable and effective learning. The integration ensures seamless data flow, with AES2S outputs enhancing EAORL state representations. Validated in a simulated smart grid environment, the architecture dynamically adjusts energy consumption, demonstrating significant improvements in energy efficiency, cost reduction, and user comfort. Evaluation metrics confirm the system's effectiveness, making AES2S-EAORL a robust solution for smart grid energy management and demand response optimization.
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Vol.:(0123456789)
SN Computer Science (2024) 5:1017
https://doi.org/10.1007/s42979-024-03305-2
SN Computer Science
ORIGINAL RESEARCH
Integrated Architecture forSmart Grid Energy Management: Deep
Attention‑Enhanced Sequence‑to‑Sequence Model withEnergy‑Aware
Optimized Reinforcement Learning forDemand Response
K.R.Deepa1· N.Thillaiarasu1
Received: 2 August 2024 / Accepted: 10 September 2024
© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024
Abstract
Demand Response (DR) has become a key strategy for enhancing energy system sustainability and reducing costs. Deep
Learning (DL) has emerged as crucial for managing DR's complexity and large data volumes, enabling near real-time
decision-making. DL techniques can effectively tackle challenges such as selecting responsive users, understanding con-
sumption behaviours, optimizing pricing, monitoring and controlling devices, engaging more consumers in DR schemes, and
determining fair remuneration for participants. This research work presents an integrated architecture for smart grid energy
management, combining a Deep Attention-Enhanced Sequence-to-Sequence Model (AES2S) with Energy-Aware Optimized
Reinforcement Learning (EAORL). The objective is to design a system that performs non-intrusive load monitoring and
optimizes demand response to enhance energy efficiency while maintaining user comfort. The AES2S module accurately
performs appliance state identification and load disaggregation using convolutional layers, Enhanced Sequence-to-Sequence
Model networks, and an attention mechanism. The EAORL module employs a multi-agent system, where each agent uses
a Deep Q-Learning Network to learn optimal policies for adjusting energy consumption in response to grid conditions and
user demand. The system uses an Iterative Policy Update mechanism, where agents update their policies sequentially, ensur-
ing stable and effective learning. The integration ensures seamless data flow, with AES2S outputs enhancing EAORL state
representations. Validated in a simulated smart grid environment, the architecture dynamically adjusts energy consumption,
demonstrating significant improvements in energy efficiency, cost reduction, and user comfort. Evaluation metrics confirm
the system's effectiveness, making AES2S-EAORL a robust solution for smart grid energy management and demand response
optimization.
Keywords Smart grid· Energy management· Deep learning· Reinforcement learning· Non-intrusive load monitoring·
Demand response· Attention mechanism
Introduction
The global population is experiencing growth, concur-
rent with advancements in technology that are driving an
increase in energy consumption [1]. The awareness of the
finite nature of fossil fuels is of utmost importance. Moreo-
ver, numerous studies have consistently demonstrated that
environmental factors account for 27% of pollutant emis-
sions, posing a potential threat to the ecosystem and exacer-
bating the issue of global warming. In order to address the
increasing need for energy and decrease reliance on fossil
fuels, it is crucial to incorporate load prediction and energy
management optimization. The smart grid (SG) is comprised
of various components, including smart meters, control sys-
tems, advanced sensing technologies, and communication
technologies. The combination of these components enables
the operation of an advanced electrical network. The system
operates as an advanced and innovative energy solution for
the future. The development of the smart grid concept aims
to ensure effective load control, as well as efficient power
generation and distribution [2, 3]. The bidirectional flow
of data and energy between the customer and the energy
* K. R. Deepa
deepas.ravi@gmail.com
N. Thillaiarasu
Thillai888@gmail.com
1 School ofC&IT, REVA University, Bangalore, Karnataka,
India
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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