Predicting Short Term Energy Demand in Smart Grid: A Deep Learning Approach for Integrating Renewable Energy Sources in Line with SDGs 7, 9, and 13

• URL: http://arxiv.org/abs/2304.03997v4
• Date: Tue, 20 Aug 2024 10:34:28 GMT
• Title: Predicting Short Term Energy Demand in Smart Grid: A Deep Learning Approach for Integrating Renewable Energy Sources in Line with SDGs 7, 9, and 13
• Authors: Md Saef Ullah Miah, Junaida Sulaiman, Md. Imamul Islam, Md. Masuduzzaman, Molla Shahadat Hossain Lipu, Ramdhan Nugraha,
• Abstract summary: We propose a deep learning model for predicting energy demand in a smart power grid. We use long short-term memory networks to capture complex patterns and dependencies in energy demand data. The proposed model can accurately predict energy demand with a mean absolute error of 1.4%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Integrating renewable energy sources into the power grid is becoming increasingly important as the world moves towards a more sustainable energy future in line with SDG 7. However, the intermittent nature of renewable energy sources can make it challenging to manage the power grid and ensure a stable supply of electricity, which is crucial for achieving SDG 9. In this paper, we propose a deep learning model for predicting energy demand in a smart power grid, which can improve the integration of renewable energy sources by providing accurate predictions of energy demand. Our approach aligns with SDG 13 on climate action, enabling more efficient management of renewable energy resources. We use long short-term memory networks, well-suited for time series data, to capture complex patterns and dependencies in energy demand data. The proposed approach is evaluated using four historical short-term energy demand data datasets from different energy distribution companies, including American Electric Power, Commonwealth Edison, Dayton Power and Light, and Pennsylvania-New Jersey-Maryland Interconnection. The proposed model is compared with three other state-of-the-art forecasting algorithms: Facebook Prophet, Support Vector Regression, and Random Forest Regression. The experimental results show that the proposed REDf model can accurately predict energy demand with a mean absolute error of 1.4%, indicating its potential to enhance the stability and efficiency of the power grid and contribute to achieving SDGs 7, 9, and 13. The proposed model also has the potential to manage the integration of renewable energy sources effectively.

Related papers

• A Survey of AI-Powered Mini-Grid Solutions for a Sustainable Future in Rural Communities [0.18783379094746652]
  This paper reviews various forecasting models, including statistical methods, machine learning algorithms, and hybrid approaches. It explores public datasets and tools such as Prophet, NeuralProphet, and N-BEATS for model implementation and validation. The survey concludes with recommendations for future research, addressing challenges in model adaptation and optimisation for real-world applications.
  arXiv  Detail & Related papers  (2024-07-17T20:23:38Z)
• Empowering Distributed Solutions in Renewable Energy Systems and Grid Optimization [3.8979646385036175]
  Machine learning (ML) advancements play a crucial role in empowering renewable energy sources and improving grid management. The incorporation of big data and ML into smart grids offers several advantages, including heightened energy efficiency. However, challenges like handling large data volumes, ensuring cybersecurity, and obtaining specialized expertise must be addressed.
  arXiv  Detail & Related papers  (2023-10-24T02:45:16Z)
• A Federated learning model for Electric Energy management using Blockchain Technology [0.0]
  Energy shortfall and electricity load shedding are the main problems for developing countries. The improved energy management and use of renewable sources can be significant to resolve energy crisis. It is necessary to increase the use of renewable energy sources to meet the increasing energy demand due to high prices of fossil-fuel based energy.
  arXiv  Detail & Related papers  (2023-07-18T09:00:26Z)
• Benchmarks and Custom Package for Energy Forecasting [55.460452605056894]
  Energy forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch. In this paper, we collected large-scale load datasets and released a new renewable energy dataset. We conducted extensive experiments with 21 forecasting methods in these energy datasets at different levels under 11 evaluation metrics.
  arXiv  Detail & Related papers  (2023-07-14T06:50:02Z)
• Combating Uncertainties in Wind and Distributed PV Energy Sources Using Integrated Reinforcement Learning and Time-Series Forecasting [2.774390661064003]
  unpredictability of renewable energy generation poses challenges for electricity providers and utility companies. We propose a novel framework with two objectives: (i) combating uncertainty of renewable energy in smart grid by leveraging time-series forecasting with Long-Short Term Memory (LSTM) solutions, and (ii) establishing distributed and dynamic decision-making framework with multi-agent reinforcement learning using Deep Deterministic Policy Gradient (DDPG) algorithm.
  arXiv  Detail & Related papers  (2023-02-27T19:12:50Z)
• Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework [53.97223237572147]
  This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users.
  arXiv  Detail & Related papers  (2022-11-29T01:18:58Z)
• Evaluating Distribution System Reliability with Hyperstructures Graph Convolutional Nets [74.51865676466056]
  We show how graph convolutional networks and hyperstructures representation learning framework can be employed for accurate, reliable, and computationally efficient distribution grid planning. Our numerical experiments show that the proposed Hyper-GCNNs approach yields substantial gains in computational efficiency.
  arXiv  Detail & Related papers  (2022-11-14T01:29:09Z)
• Battery and Hydrogen Energy Storage Control in a Smart Energy Network with Flexible Energy Demand using Deep Reinforcement Learning [2.5666730153464465]
  We introduce a hybrid energy storage system composed of battery and hydrogen energy storage. We propose a deep reinforcement learning-based control strategy to optimise the scheduling of the hybrid energy storage system and energy demand in real-time.
  arXiv  Detail & Related papers  (2022-08-26T16:47:48Z)
• Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
  We study a risk-aware energy scheduling problem for a microgrid-powered MEC network. We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
  arXiv  Detail & Related papers  (2020-02-21T02:14:38Z)
• Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems [87.4519172058185]
  An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
  arXiv  Detail & Related papers  (2020-02-20T04:58:07Z)
• NeurOpt: Neural network based optimization for building energy management and climate control [58.06411999767069]
  We propose a data-driven control algorithm based on neural networks to reduce this cost of model identification. We validate our learning and control algorithms on a two-story building with ten independently controlled zones, located in Italy.
  arXiv  Detail & Related papers  (2020-01-22T00:51:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.