# DA-LSTM: A Dynamic Drift-Adaptive Learning Framework for Interval Load Forecasting with LSTM Networks

arXiv: https://arxiv.org/abs/2305.08767 · HTML: https://mlsanity.com/paper/2305.08767/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 115 references that are in the map.
- First posted on arXiv 15 May 2023.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [Compacting, Picking and Growing for Unforgetting Continual Learning](https://mlsanity.com/paper/1910.06562/) (2019)
- [On the State of the Art of Evaluation in Neural Language Models](https://mlsanity.com/paper/1707.05589/) (2017)
- [A Tutorial on Kernel Density Estimation and Recent Advances](https://mlsanity.com/paper/1704.03924/) (2017)
- [Characterizing Concept Drift](https://mlsanity.com/paper/1511.03816/) (2015)
- [From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors](https://mlsanity.com/paper/2203.11070/) (2022)
- [Adaptive Methods for Short-Term Electricity Load Forecasting During COVID-19 Lockdown in France](https://mlsanity.com/paper/2009.06527/) (2020)
- [Optimizing Performance of Recurrent Neural Networks on GPUs](https://mlsanity.com/paper/1604.01946/) (2016)
- [Learning Parameter Distributions to Detect Concept Drift in Data Streams](https://mlsanity.com/paper/2010.09388/) (2020)
- [LSTM Benchmarks for Deep Learning Frameworks](https://mlsanity.com/paper/1806.01818/) (2018)

## Built on by (most cited first, 8 of 8)

- [Short-Term Electricity-Load Forecasting by Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2408.16202/) (2024)
- [Adaptive Data Quality Scoring Operations Framework using Drift-Aware Mechanism for Industrial Applications](https://mlsanity.com/paper/2408.06724/) (2024)
- [Thwarting Cybersecurity Attacks with Explainable Concept Drift](https://mlsanity.com/paper/2403.13023/) (2024)
- [Enhanced Photovoltaic Power Forecasting: An iTransformer and LSTM-Based Model Integrating Temporal and Covariate Interactions](https://mlsanity.com/paper/2412.02302/) (2024)
- [End-to-End Data Quality-Driven Framework for Machine Learning in Production Environment](https://mlsanity.com/paper/2512.19723/) (2025)
- [Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles](https://mlsanity.com/paper/2506.13828/) (2025)
- [Data-Driven Heat Pump Management: Combining Machine Learning with Anomaly Detection for Residential Hot Water Systems](https://mlsanity.com/paper/2506.15719/) (2025)
- [Resilient Load Forecasting under Climate Change: Adaptive Conditional Neural Processes for Few-Shot Extreme Load Forecasting](https://mlsanity.com/paper/2602.04609/) (2026)

## Abstract (arXiv)

Load forecasting is a crucial topic in energy management systems (EMS) due to its vital role in optimizing energy scheduling and enabling more flexible and intelligent power grid systems. As a result, these systems allow power utility companies to respond promptly to demands in the electricity market. Deep learning (DL) models have been commonly employed in load forecasting problems supported by adaptation mechanisms to cope with the changing pattern of consumption by customers, known as concept drift. A drift magnitude threshold should be defined to design change detection methods to identify drifts. While the drift magnitude in load forecasting problems can vary significantly over time, existing literature often assumes a fixed drift magnitude threshold, which should be dynamically adjusted rather than fixed during system evolution. To address this gap, in this paper, we propose a dynamic drift-adaptive Long Short-Term Memory (DA-LSTM) framework that can improve the performance of load forecasting models without requiring a drift threshold setting. We integrate several strategies into the framework based on active and passive adaptation approaches. To evaluate DA-LSTM in real-life settings, we thoroughly analyze the proposed framework and deploy it in a real-world problem through a cloud-based environment. Efficiency is evaluated in terms of the prediction performance of each approach and computational cost. The experiments show performance improvements on multiple evaluation metrics achieved by our framework compared to baseline methods from the literature. Finally, we present a trade-off analysis between prediction performance and computational costs.