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

2025-01-06

AI Summary

Existing residential load forecasting methods rely heavily on dense, high-frequency data and struggle to leverage sparse event-based knowledge. To address this limitation, we propose the Event-Response Knowledge-Guided (ERKG) framework, which pioneers modeling appliance operational states as transferable, sparse knowledge sources. ERKG introduces a plug-and-play knowledge fusion mechanism to explicitly and dynamically capture user behavioral patterns. The framework comprises three core modules: event detection, state sequence estimation, and temporal knowledge-guided fusion—designed to be compatible with mainstream time-series models (e.g., LSTM, Transformer) and support end-to-end training. Evaluated on multiple real-world datasets, ERKG achieves an average MAE reduction of over 8% compared to state-of-the-art methods. Ablation studies confirm that the event-response modeling component is the primary driver of performance gains, validating its effectiveness in harnessing sparse behavioral cues for accurate load forecasting.

Technology Category

• Intelligent Robots: State Estimation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Time-Series/Data Streams

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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. The source code will be available at https://github.com/ergoucao/ERKG.

Problem

• Home Energy Prediction
• Sparse Knowledge
• Electrical Appliances Usage

Innovation

• ERKG
• Electricity Consumption Prediction
• Event Reaction