From Dense to Sparse: Event Response for Enhanced Residential Load Forecasting

• URL: http://arxiv.org/abs/2501.02781v3
• Date: Wed, 08 Jan 2025 11:16:18 GMT
• Title: From Dense to Sparse: Event Response for Enhanced Residential Load Forecasting
• Authors: Xin Cao, Qinghua Tao, Yingjie Zhou, Lu Zhang, Le Zhang, Dongjin Song, Dapeng Oliver Wu, Ce Zhu,
• Abstract summary: We propose an Event-Response Knowledge Guided approach (ERKG) for residential load forecasting. ERKG incorporates the estimation of electricity usage events for different appliances, mining event-related sparse knowledge from the load series.
• Score: 48.22398304557558
• License:
• Abstract: Residential load forecasting (RLF) is crucial for resource scheduling in power systems. Most existing methods utilize all given load records (dense data) to indiscriminately extract the dependencies between historical and future time series. However, there exist important regular patterns residing in the event-related associations among different appliances (sparse knowledge), which have yet been ignored. In this paper, we propose an Event-Response Knowledge Guided approach (ERKG) for RLF by incorporating the estimation of electricity usage events for different appliances, mining event-related sparse knowledge from the load series. With ERKG, the event-response estimation enables portraying the electricity consumption behaviors of residents, revealing regular variations in appliance operational states. To be specific, ERKG consists of knowledge extraction and guidance: i) a forecasting model is designed for the electricity usage events by estimating appliance operational states, aiming to extract the event-related sparse knowledge; ii) a novel knowledge-guided mechanism is established by fusing such state estimates of the appliance events into the RLF model, which can give particular focuses on the patterns of users' electricity consumption behaviors. Notably, ERKG can flexibly serve as a plug-in module to boost the capability of existing forecasting models by leveraging event response. In numerical experiments, extensive comparisons and ablation studies have verified the effectiveness of our ERKG, e.g., over 8% MAE can be reduced on the tested state-of-the-art forecasting models.

Related papers

• One Model to Forecast Them All and in Entity Distributions Bind Them [0.0]
  Probabilistic forecasting in power systems often involves multi-entity datasets like households, feeders, and wind turbines. Traditional approaches require training individual models for each entity, making them inefficient and hard to scale. This study addresses this problem using GUIDE-VAE, a conditional variational autoencoder that allows entity-specific probabilistic forecasting using a single model.
  arXiv  Detail & Related papers  (2025-01-26T12:14:09Z)
• AI-Powered Predictions for Electricity Load in Prosumer Communities [0.0]
  We present and test artificial intelligence powered short-term load forecasting methodologies. Results show that the combination of persistent and regression terms (adapted to the load forecasting task) achieves the best forecast accuracy.
  arXiv  Detail & Related papers  (2024-02-21T12:23:09Z)
• Interactive Generalized Additive Model and Its Applications in Electric Load Forecasting [12.431475555894089]
  In this paper, we propose an interactive GAM which is not only interpretable but also can incorporate specific domain knowledge in electric power industry. Our interactive GAM outperforms current state-of-the-art methods and demonstrates good generalization ability in the cases of extreme weather events. We launched a user-friendly web-based tool based on interactive GAM and already incorporated it into our eForecaster product.
  arXiv  Detail & Related papers  (2023-10-24T09:17:47Z)
• Analysis of Weather and Time Features in Machine Learning-aided ERCOT Load Forecasting [0.2184775414778289]
  This work develops several machine learning (ML) models that take various time and weather information as part of the input features to predict the short-term system-wide total load. Actual load and historical weather data for the same region were processed and then used to train the ML models. Case studies demonstrated the effectiveness of ML models trained with different weather and time input features for ERCOT load forecasting.
  arXiv  Detail & Related papers  (2023-10-13T00:46:12Z)
• Exploring the Limits of Historical Information for Temporal Knowledge Graph Extrapolation [59.417443739208146]
  We propose a new event forecasting model based on a novel training framework of historical contrastive learning. CENET learns both the historical and non-historical dependency to distinguish the most potential entities. We evaluate our proposed model on five benchmark graphs.
  arXiv  Detail & Related papers  (2023-08-29T03:26:38Z)
• 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)
• Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting [98.57851612518758]
  Probabilistic time series forecasting involves estimating the distribution of future based on its history. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks. We show in experiments that our model produces accurate and sharp probabilistic forecasts.
  arXiv  Detail & Related papers  (2021-01-31T06:49:33Z)
• Activity Detection And Modeling Using Smart Meter Data: Concept And Case Studies [6.7336801526732755]
  This paper proposes a new and more effective approach, i.e., activity disaggregation. We develop a framework by leverage machine learning for activity detection based on residential load data and features. We show through numerical case studies to demonstrate the effectiveness of the activity detection method.
  arXiv  Detail & Related papers  (2020-10-26T02:36:35Z)
• Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids [67.72249211312723]
  Real-time state estimation and forecasting is critical for efficient operation of power grids. PhI-GPR is presented and used for forecasting and estimating the phase angle, angular speed, and wind mechanical power of a three-generator power grid system. We demonstrate that the proposed PhI-GPR method can accurately forecast and estimate both observed and unobserved states.
  arXiv  Detail & Related papers  (2020-10-09T14:18:31Z)
• 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)

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.