# SaDI: A Self-adaptive Decomposed Interpretable Framework for Electric Load Forecasting under Extreme Events

arXiv: https://arxiv.org/abs/2306.08299 · HTML: https://mlsanity.com/paper/2306.08299/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 14 Jun 2023.

## Builds on (most cited first, 5 of 5)

- [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://mlsanity.com/paper/2201.12740/) (2022)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [Delving into Deep Imbalanced Regression](https://mlsanity.com/paper/2102.09554/) (2021)
- [How Interpretable and Trustworthy are GAMs?](https://mlsanity.com/paper/2006.06466/) (2020)
- [The ASHRAE Great Energy Predictor III competition: Overview and results](https://mlsanity.com/paper/2007.06933/) (2020)

## Built on by (most cited first, 21 of 21)

- [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://mlsanity.com/paper/2310.01728/) (2023)
- [Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook](https://mlsanity.com/paper/2310.10196/) (2023)
- [TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents](https://mlsanity.com/paper/2502.11418/) (2025)
- [TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Models](https://mlsanity.com/paper/2509.24803/) (2025)
- [TimeSeriesScientist: A General-Purpose AI Agent for Time Series Analysis](https://mlsanity.com/paper/2510.01538/) (2025)
- [S2IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting](https://mlsanity.com/paper/2403.05798/) (2024)
- [Agentic Retrieval-Augmented Generation for Time Series Analysis](https://mlsanity.com/paper/2408.14484/) (2024)
- [TimeMosaic: Temporal Heterogeneity Guided Time Series Forecasting via Adaptive Granularity Patch and Segment-wise Decoding](https://mlsanity.com/paper/2509.19406/) (2025)
- [BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition](https://mlsanity.com/paper/2308.14906/) (2023)
- [Vision-Enhanced Time Series Forecasting via Latent Diffusion Models](https://mlsanity.com/paper/2502.14887/) (2025)
- [T3Time: Tri-Modal Time Series Forecasting via Adaptive Multi-Head Alignment and Residual Fusion](https://mlsanity.com/paper/2508.04251/) (2025)
- [Sparse-VQ Transformer: An FFN-Free Framework with Vector Quantization for Enhanced Time Series Forecasting](https://mlsanity.com/paper/2402.05830/) (2024)
- [Enhancing Time Series Forecasting via Multi-Level Text Alignment with LLMs](https://mlsanity.com/paper/2504.07360/) (2025)
- [Dual-Forecaster: A Multimodal Time Series Model Integrating Descriptive and Predictive Texts](https://mlsanity.com/paper/2505.01135/) (2025)
- [VITRO: Vocabulary Inversion for Time-series Representation Optimization](https://mlsanity.com/paper/2412.17921/) (2024)
- [xTime: Extreme Event Prediction with Hierarchical Knowledge Distillation and Expert Fusion](https://mlsanity.com/paper/2510.20651/) (2025)
- [STELLA: Guiding Large Language Models for Time Series Forecasting with Semantic Abstractions](https://mlsanity.com/paper/2512.04871/) (2025)
- [M2FMoE: Multi-Resolution Multi-View Frequency Mixture-of-Experts for Extreme-Adaptive Time Series Forecasting](https://mlsanity.com/paper/2601.08631/) (2026)
- [Resilient Load Forecasting under Climate Change: Adaptive Conditional Neural Processes for Few-Shot Extreme Load Forecasting](https://mlsanity.com/paper/2602.04609/) (2026)
- [Deep TPC: Temporal-Prior Conditioning for Time Series Forecasting](https://mlsanity.com/paper/2602.16188/) (2026)
- [DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data](https://mlsanity.com/paper/2605.17866/) (2026)

## Abstract (arXiv)

Accurate prediction of electric load is crucial in power grid planning and management. In this paper, we solve the electric load forecasting problem under extreme events such as scorching heats. One challenge for accurate forecasting is the lack of training samples under extreme conditions. Also load usually changes dramatically in these extreme conditions, which calls for interpretable model to make better decisions. In this paper, we propose a novel forecasting framework, named Self-adaptive Decomposed Interpretable framework~(SaDI), which ensembles long-term trend, short-term trend, and period modelings to capture temporal characteristics in different components. The external variable triggered loss is proposed for the imbalanced learning under extreme events. Furthermore, Generalized Additive Model (GAM) is employed in the framework for desirable interpretability. The experiments on both Central China electric load and public energy meters from buildings show that the proposed SaDI framework achieves average 22.14% improvement compared with the current state-of-the-art algorithms in forecasting under extreme events in terms of daily mean of normalized RMSE. Code, Public datasets, and Appendix are available at: https://doi.org/10.24433/CO.9696980.v1 .