Season-Independent PV Disaggregation Using Multi-Scale Net Load Temporal Feature Extraction and Weather Factor Fusion
- URL: http://arxiv.org/abs/2505.18747v1
- Date: Sat, 24 May 2025 15:25:46 GMT
- Title: Season-Independent PV Disaggregation Using Multi-Scale Net Load Temporal Feature Extraction and Weather Factor Fusion
- Authors: Xiaolu Chen, Chenghao Huang, Yanru Zhang, Hao Wang,
- Abstract summary: This paper proposes a PV disaggregation method that integrates Hierarchical Interpolation (HI) and multi-head self-attention mechanisms.<n>By using HI to extract net load features and multi-head self-attention to capture the complex dependencies between weather factors, the method achieves precise PV generation predictions.
- Score: 13.146806294562474
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: With the advancement of energy Internet and energy system integration, the increasing adoption of distributed photovoltaic (PV) systems presents new challenges on smart monitoring and measurement for utility companies, particularly in separating PV generation from net electricity load. Existing methods struggle with feature extraction from net load and capturing the relevance between weather factors. This paper proposes a PV disaggregation method that integrates Hierarchical Interpolation (HI) and multi-head self-attention mechanisms. By using HI to extract net load features and multi-head self-attention to capture the complex dependencies between weather factors, the method achieves precise PV generation predictions. Simulation experiments demonstrate the effectiveness of the proposed method in real-world data, supporting improved monitoring and management of distributed energy systems.
Related papers
- Smart Energy Guardian: A Hybrid Deep Learning Model for Detecting Fraudulent PV Generation [13.146806294562474]
We propose an efficient Electricity Theft Detection (ETD) method that accurately identifies fraudulent behaviors in residential PV generation.<n>Our hybrid deep learning model, combining CNN, Long Short-Term Memory (LSTM), and Transformer, excels in capturing both short-term and long-term temporal dependencies.
arXiv Detail & Related papers (2025-05-24T15:47:00Z) - Clustering Rooftop PV Systems via Probabilistic Embeddings [0.0]
Large, spatially distributed time-series data is both high-dimensional and affected by missing values.<n>Probability entity embedding-based clustering framework is proposed to address these problems.<n> Applied to a multi-year residential PV dataset, it produces uncertainty-aware cluster profiles that outperform a physics-based baseline in representativeness and robustness.
arXiv Detail & Related papers (2025-05-15T20:44:45Z) - Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach [50.52139512096988]
6G Internet of Things (IoT) networks face challenges in remote areas and disaster scenarios where ground infrastructure is unavailable.<n>This paper proposes a novel aerial unmanned vehicle (UAV)-assisted computing (MEC) system enhanced by directional antennas to provide both computational and energy support for ground edge terminals.
arXiv Detail & Related papers (2025-05-06T06:46:19Z) - Privacy-Preserving Personalized Federated Learning for Distributed Photovoltaic Disaggregation under Statistical Heterogeneity [13.146806294562474]
A privacy-preserving distributed PV disaggregation framework is proposed using Personalized Federated Learning (PFL)<n>The proposed method employs a two-level framework that combines local and global modeling.<n>Experiments on real-world data demonstrate the effectiveness of this proposed framework, showing improved accuracy and robustness compared to benchmark methods.
arXiv Detail & Related papers (2025-04-25T05:09:27Z) - Distributed Multi-Head Learning Systems for Power Consumption Prediction [59.293903039988884]
We propose Distributed Multi-Head learning (DMH) systems for power consumption prediction in smart factories.<n>DMH systems are designed as distributed and split learning, reducing the client-to-server transmission cost.<n>DMH-E system reduces the error of the state-of-the-art systems by 14.5% to 24.0%.
arXiv Detail & Related papers (2025-01-21T13:46:23Z) - Deep Generative Methods for Producing Forecast Trajectories in Power
Systems [0.0]
Transport System Operators (TSOs) must conduct analyses to simulate the future functioning of power systems.
These simulations are used as inputs in decision-making processes.
arXiv Detail & Related papers (2023-09-26T14:43:01Z) - MATNet: Multi-Level Fusion Transformer-Based Model for Day-Ahead PV
Generation Forecasting [0.47518865271427785]
MATNet is a novel self-attention transformer-based architecture for PV power generation forecasting.
It consists of a hybrid approach that combines the AI paradigm with the prior physical knowledge of PV power generation.
Results show that our proposed architecture significantly outperforms the current state-of-the-art methods.
arXiv Detail & Related papers (2023-06-17T14:03:09Z) - Long-term Wind Power Forecasting with Hierarchical Spatial-Temporal
Transformer [112.12271800369741]
Wind power is attracting increasing attention around the world due to its renewable, pollution-free, and other advantages.
Accurate wind power forecasting (WPF) can effectively reduce power fluctuations in power system operations.
Existing methods are mainly designed for short-term predictions and lack effective spatial-temporal feature augmentation.
arXiv Detail & Related papers (2023-05-30T04:03:15Z) - Evaluating the Planning and Operational Resilience of Electrical
Distribution Systems with Distributed Energy Resources using Complex Network
Theory [0.0]
This paper proposes a methodology to evaluate the planning and operational resilience of power distribution systems under extreme events.
The proposed framework is developed by effectively employing the complex network theory.
arXiv Detail & Related papers (2022-08-24T13:41:37Z) - Stochastically forced ensemble dynamic mode decomposition for
forecasting and analysis of near-periodic systems [65.44033635330604]
We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system.
We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony.
Results are presented for a test case using load data from an electrical grid.
arXiv Detail & Related papers (2020-10-08T20:25:52Z) - 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) - Targeted free energy estimation via learned mappings [66.20146549150475]
Free energy perturbation (FEP) was proposed by Zwanzig more than six decades ago as a method to estimate free energy differences.
FEP suffers from a severe limitation: the requirement of sufficient overlap between distributions.
One strategy to mitigate this problem, called Targeted Free Energy Perturbation, uses a high-dimensional mapping in configuration space to increase overlap.
arXiv Detail & Related papers (2020-02-12T11:10:00Z)
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.