Clustering Rooftop PV Systems via Probabilistic Embeddings

• URL: http://arxiv.org/abs/2505.10699v1
• Date: Thu, 15 May 2025 20:44:45 GMT
• Title: Clustering Rooftop PV Systems via Probabilistic Embeddings
• Authors: Kutay Bölat, Tarek Alskaif, Peter Palensky, Simon Tindemans,
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As the number of rooftop photovoltaic (PV) installations increases, aggregators and system operators are required to monitor and analyze these systems, raising the challenge of integration and management of large, spatially distributed time-series data that are both high-dimensional and affected by missing values. In this work, a probabilistic entity embedding-based clustering framework is proposed to address these problems. This method encodes each PV system's characteristic power generation patterns and uncertainty as a probability distribution, then groups systems by their statistical distances and agglomerative clustering. Applied to a multi-year residential PV dataset, it produces concise, uncertainty-aware cluster profiles that outperform a physics-based baseline in representativeness and robustness, and support reliable missing-value imputation. A systematic hyperparameter study further offers practical guidance for balancing model performance and robustness.

Related papers

• Solar Photovoltaic Assessment with Large Language Model [6.72184534513047]
  We investigate how large language models (LLMs) can be leveraged to overcome solar panel detection challenges.<n>LLMs face several challenges in solar panel detection, including difficulties with multi-step logical processes.<n>We propose the PV Assessment with LLMs framework, which incorporates task decomposition for more efficient output standardization.
  arXiv  Detail & Related papers  (2025-07-25T10:26:29Z)
• Season-Independent PV Disaggregation Using Multi-Scale Net Load Temporal Feature Extraction and Weather Factor Fusion [13.146806294562474]
  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.
  arXiv  Detail & Related papers  (2025-05-24T15:25:46Z)
• 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)
• Improving $(α, f)$-Byzantine Resilience in Federated Learning via layerwise aggregation and cosine distance [7.8973037023478785]
  Federated Learning (FL) is a potential solution to data privacy challenges in distributed machine learning.<n>FL systems remain vulnerable to Byzantine attacks, where malicious nodes contribute corrupted model updates.<n>This paper introduces Layerwise Cosine Aggregation, a novel aggregation scheme designed to enhance robustness of these rules in high-dimensional settings.
  arXiv  Detail & Related papers  (2025-03-27T08:07:39Z)
• Meta-Statistical Learning: Supervised Learning of Statistical Inference [59.463430294611626]
  This work demonstrates that the tools and principles driving the success of large language models (LLMs) can be repurposed to tackle distribution-level tasks.<n>We propose meta-statistical learning, a framework inspired by multi-instance learning that reformulates statistical inference tasks as supervised learning problems.
  arXiv  Detail & Related papers  (2025-02-17T18:04:39Z)
• Interaction-Aware Gaussian Weighting for Clustered Federated Learning [58.92159838586751]
  Federated Learning (FL) emerged as a decentralized paradigm to train models while preserving privacy.<n>We propose a novel clustered FL method, FedGWC (Federated Gaussian Weighting Clustering), which groups clients based on their data distribution.<n>Our experiments on benchmark datasets show that FedGWC outperforms existing FL algorithms in cluster quality and classification accuracy.
  arXiv  Detail & Related papers  (2025-02-05T16:33:36Z)
• An Agglomerative Clustering of Simulation Output Distributions Using Regularized Wasserstein Distance [0.0]
  We present a novel agglomerative clustering algorithm that utilizes the regularized Wasserstein distance to cluster simulation outputs. This framework has several important use cases, including anomaly detection, pre-optimization, and online monitoring.
  arXiv  Detail & Related papers  (2024-07-16T18:07:32Z)
• AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning [98.26836657967162]
  textbfAgentOhana aggregates agent trajectories from distinct environments, spanning a wide array of scenarios. textbfxLAM-v0.1, a large action model tailored for AI agents, demonstrates exceptional performance across various benchmarks.
  arXiv  Detail & Related papers  (2024-02-23T18:56:26Z)
• 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)
• Probabilistic electric load forecasting through Bayesian Mixture Density Networks [70.50488907591463]
  Probabilistic load forecasting (PLF) is a key component in the extended tool-chain required for efficient management of smart energy grids. We propose a novel PLF approach, framed on Bayesian Mixture Density Networks. To achieve reliable and computationally scalable estimators of the posterior distributions, both Mean Field variational inference and deep ensembles are integrated.
  arXiv  Detail & Related papers  (2020-12-23T16:21:34Z)
• 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)
• Bayesian Sparse Factor Analysis with Kernelized Observations [67.60224656603823]
  Multi-view problems can be faced with latent variable models. High-dimensionality and non-linear issues are traditionally handled by kernel methods. We propose merging both approaches into single model.
  arXiv  Detail & Related papers  (2020-06-01T14:25:38Z)
• ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundings [54.33327082243022]
  ClusterVO is a stereo Visual Odometry which simultaneously clusters and estimates the motion of both ego and surrounding rigid clusters/objects. Unlike previous solutions relying on batch input or imposing priors on scene structure or dynamic object models, ClusterVO is online, general and thus can be used in various scenarios including indoor scene understanding and autonomous driving.
  arXiv  Detail & Related papers  (2020-03-29T09:06:28Z)

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.