ClimateEdge: a lightweight weather-augmented LSTM framework for real-time demand response in climate-stressed smart grids

Extreme weather events driven by climate change impose non-linear load surges on electricity grids at the moments when accurate forecasting is most critical and cloud-based inference latency is unacceptable. This paper presents ClimateEdge, a lightweight framework that integrates weather-augmented feature construction, a Climate-Enhanced Long Short-Term Memory (CE-LSTM) architecture with temporal attention, and an INT8 post-training quantization pipeline to enable real-time demand response inference on an NVIDIA Jetson Nano edge device. The framework is evaluated on two grid datasets spanning contrasting climate scenarios: the California Independent System Operator (CAISO) summer heatwave corpus, covering June 2020 to September 2022 at hourly resolution and containing 20,422 records with 1,228 extreme-heat events, and the ISO New England (ISONE) winter cold-snap corpus, covering July 2023 to February 2024 and containing 5,813 records with 210 extreme-cold events. In both cases the CE-LSTM outperforms five baseline models including a full-size iTransformer, achieving a Mean Absolute Error (MAE) of 471.8 MW and a Mean Absolute Percentage Error (MAPE) of 1.55% on the CAISO extreme-weather partition, representing a 21.1% improvement over the next-best baseline, and an MAE of 329.6 MW and MAPE of 2.31% on the ISONE extreme-weather partition, a 19.4% improvement. The INT8-quantized CE-LSTM runs at 9.2 ms per inference step with a runtime memory footprint of 8.1 MB and a power draw of 3.4 W on the Jetson Nano, achieving a 3.65 × latency speedup over the FP32 baseline at a cost of less than 1% relative MAE increase. Ablation studies confirm that the weather-augmented features account for the largest share of the accuracy improvement, and that temporal attention up-weights climate-stressed timesteps during both heatwave and cold-snap events.