Physics-informed Convolutional Neural Network for Microgrid Economic Dispatch
- URL: http://arxiv.org/abs/2404.18362v2
- Date: Thu, 2 May 2024 03:22:29 GMT
- Title: Physics-informed Convolutional Neural Network for Microgrid Economic Dispatch
- Authors: Xiaoyu Ge, Javad Khazaei,
- Abstract summary: This study proposes using a convolutional neural network (CNN) based on deep learning to solve numerical optimization problems in real-time.
CNN is more efficient, delivers more dependable results, and has a shorter response time when dealing with uncertainties.
A physics-inspired CNN model is developed by incorporating constraints of the ED problem into the CNN training to ensure that the model follows physical laws while fitting the data.
- Score: 1.5193212081459277
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The variability of renewable energy generation and the unpredictability of electricity demand create a need for real-time economic dispatch (ED) of assets in microgrids. However, solving numerical optimization problems in real-time can be incredibly challenging. This study proposes using a convolutional neural network (CNN) based on deep learning to address these challenges. Compared to traditional methods, CNN is more efficient, delivers more dependable results, and has a shorter response time when dealing with uncertainties. While CNN has shown promising results, it does not extract explainable knowledge from the data. To address this limitation, a physics-inspired CNN model is developed by incorporating constraints of the ED problem into the CNN training to ensure that the model follows physical laws while fitting the data. The proposed method can significantly accelerate real-time economic dispatch of microgrids without compromising the accuracy of numerical optimization techniques. The effectiveness of the proposed data-driven approach for optimal allocation of microgrid resources in real-time is verified through a comprehensive comparison with conventional numerical optimization approaches.
Related papers
- SAfEPaTh: A System-Level Approach for Efficient Power and Thermal Estimation of Convolutional Neural Network Accelerator [4.1221717424687165]
This paper introduces SAfEPaTh, a novel system-level approach for accurately estimating power and temperature in tile-based CNN accelerators.
By addressing both steady-state and transient-state scenarios, SAfEPaTh effectively captures the dynamic effects of pipeline bubbles in interlayer pipelines.
arXiv Detail & Related papers (2024-07-24T20:29:52Z) - Bayesian Entropy Neural Networks for Physics-Aware Prediction [14.705526856205454]
We introduce BENN, a framework designed to impose constraints on Bayesian Neural Network (BNN) predictions.
Benn is capable of constraining not only the predicted values but also their derivatives and variances, ensuring a more robust and reliable model output.
Results highlight significant improvements over traditional BNNs and showcase competitive performance relative to contemporary constrained deep learning methods.
arXiv Detail & Related papers (2024-07-01T07:00:44Z) - Iterative Sizing Field Prediction for Adaptive Mesh Generation From Expert Demonstrations [49.173541207550485]
Adaptive Meshing By Expert Reconstruction (AMBER) is an imitation learning problem.
AMBER combines a graph neural network with an online data acquisition scheme to predict the projected sizing field of an expert mesh.
We experimentally validate AMBER on 2D meshes and 3D meshes provided by a human expert, closely matching the provided demonstrations and outperforming a single-step CNN baseline.
arXiv Detail & Related papers (2024-06-20T10:01:22Z) - Physics-Informed Neural Networks with Hard Linear Equality Constraints [9.101849365688905]
This work proposes a novel physics-informed neural network, KKT-hPINN, which rigorously guarantees hard linear equality constraints.
Experiments on Aspen models of a stirred-tank reactor unit, an extractive distillation subsystem, and a chemical plant demonstrate that this model can further enhance the prediction accuracy.
arXiv Detail & Related papers (2024-02-11T17:40:26Z) - Solving Large-scale Spatial Problems with Convolutional Neural Networks [88.31876586547848]
We employ transfer learning to improve training efficiency for large-scale spatial problems.
We propose that a convolutional neural network (CNN) can be trained on small windows of signals, but evaluated on arbitrarily large signals with little to no performance degradation.
arXiv Detail & Related papers (2023-06-14T01:24:42Z) - Semantic Communication Enabling Robust Edge Intelligence for
Time-Critical IoT Applications [87.05763097471487]
This paper aims to design robust Edge Intelligence using semantic communication for time-critical IoT applications.
We analyze the effect of image DCT coefficients on inference accuracy and propose the channel-agnostic effectiveness encoding for offloading.
arXiv Detail & Related papers (2022-11-24T20:13:17Z) - Fast Exploration of the Impact of Precision Reduction on Spiking Neural
Networks [63.614519238823206]
Spiking Neural Networks (SNNs) are a practical choice when the target hardware reaches the edge of computing.
We employ an Interval Arithmetic (IA) model to develop an exploration methodology that takes advantage of the capability of such a model to propagate the approximation error.
arXiv Detail & Related papers (2022-11-22T15:08:05Z) - An Improved Structured Mesh Generation Method Based on Physics-informed
Neural Networks [13.196871939441273]
As numerical algorithms become more efficient and computers become more powerful, the percentage of time devoted to mesh generation becomes higher.
In this paper, we present an improved structured mesh generation method.
The method formulates the meshing problem as a global optimization problem related to a physics-informed neural network.
arXiv Detail & Related papers (2022-10-18T02:45:14Z) - Learning from Images: Proactive Caching with Parallel Convolutional
Neural Networks [94.85780721466816]
A novel framework for proactive caching is proposed in this paper.
It combines model-based optimization with data-driven techniques by transforming an optimization problem into a grayscale image.
Numerical results show that the proposed scheme can reduce 71.6% computation time with only 0.8% additional performance cost.
arXiv Detail & Related papers (2021-08-15T21:32:47Z) - Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge
Computing: A Contextual-Bandit Approach [81.5261621619557]
We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC)
We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer.
Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
arXiv Detail & Related papers (2021-08-09T08:45:47Z) - Mitigating severe over-parameterization in deep convolutional neural
networks through forced feature abstraction and compression with an
entropy-based heuristic [7.503338065129185]
We propose an Entropy-Based Convolutional Layer Estimation (EBCLE) which is robust and simple.
We present empirical evidence to emphasize the relative effectiveness of broader, yet shallower models trained using the EBCLE.
arXiv Detail & Related papers (2021-06-27T10:34:39Z)
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.