Probabilistic Forecasting for Building Energy Systems using Time-Series Foundation Models

• URL: http://arxiv.org/abs/2506.00630v1
• Date: Sat, 31 May 2025 16:38:29 GMT
• Title: Probabilistic Forecasting for Building Energy Systems using Time-Series Foundation Models
• Authors: Young Jin Park, Francois Germain, Jing Liu, Ye Wang, Toshiaki Koike-Akino, Gordon Wichern, Navid Azizan, Christopher R. Laughman, Ankush Chakrabarty,
• Abstract summary: Building energy systems critically depend on the predictive accuracy of relevant time-series models.<n>This paper investigates the applicability and fine-tuning strategies of time-series foundation models (TSFMs) in building energy forecasting.
• Score: 20.57107693396709
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Decision-making in building energy systems critically depends on the predictive accuracy of relevant time-series models. In scenarios lacking extensive data from a target building, foundation models (FMs) represent a promising technology that can leverage prior knowledge from vast and diverse pre-training datasets to construct accurate probabilistic predictors for use in decision-making tools. This paper investigates the applicability and fine-tuning strategies of time-series foundation models (TSFMs) in building energy forecasting. We analyze both full fine-tuning and parameter-efficient fine-tuning approaches, particularly low-rank adaptation (LoRA), by using real-world data from a commercial net-zero energy building to capture signals such as room occupancy, carbon emissions, plug loads, and HVAC energy consumption. Our analysis reveals that the zero-shot predictive performance of TSFMs is generally suboptimal. To address this shortcoming, we demonstrate that employing either full fine-tuning or parameter-efficient fine-tuning significantly enhances forecasting accuracy, even with limited historical data. Notably, fine-tuning with low-rank adaptation (LoRA) substantially reduces computational costs without sacrificing accuracy. Furthermore, fine-tuned TSFMs consistently outperform state-of-the-art deep forecasting models (e.g., temporal fusion transformers) in accuracy, robustness, and generalization across varying building zones and seasonal conditions. These results underline the efficacy of TSFMs for practical, data-constrained building energy management systems, enabling improved decision-making in pursuit of energy efficiency and sustainability.

Related papers

• IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting [0.0]
  This study evaluates forecasting models ranging from classical regression techniques to advanced deep learning architectures.<n>The dataset includes two years of historical electricity load data, alongside temperature and global horizontal irradiance (GHI) across five sites.<n>Our results reveal that deep learning models, including TimeGPT, fail to consistently outperform simpler statistical and machine learning approaches.
  arXiv  Detail & Related papers  (2025-05-16T15:55:34Z)
• Global-Decision-Focused Neural ODEs for Proactive Grid Resilience Management [50.34345101758248]
  We propose predict-all-then-optimize-globally (PATOG), a framework that integrates outage prediction with globally optimized interventions.<n>Our approach ensures spatially and temporally coherent decision-making, improving both predictive accuracy and operational efficiency.<n>Experiments on synthetic and real-world datasets demonstrate significant improvements in outage prediction consistency and grid resilience.
  arXiv  Detail & Related papers  (2025-02-25T16:15:35Z)
• Impact of ML Optimization Tactics on Greener Pre-Trained ML Models [46.78148962732881]
  This study aims to (i) analyze image classification datasets and pre-trained models, (ii) improve inference efficiency by comparing optimized and non-optimized models, and (iii) assess the economic impact of the optimizations. We conduct a controlled experiment to evaluate the impact of various PyTorch optimization techniques (dynamic quantization, torch.compile, local pruning, and global pruning) to 42 Hugging Face models for image classification. Dynamic quantization demonstrates significant reductions in inference time and energy consumption, making it highly suitable for large-scale systems.
  arXiv  Detail & Related papers  (2024-09-19T16:23:03Z)
• Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting Models [68.23649978697027]
  Forecast-PEFT is a fine-tuning strategy that freezes the majority of the model's parameters, focusing adjustments on newly introduced prompts and adapters. Our experiments show that Forecast-PEFT outperforms traditional full fine-tuning methods in motion prediction tasks. Forecast-FT further improves prediction performance, evidencing up to a 9.6% enhancement over conventional baseline methods.
  arXiv  Detail & Related papers  (2024-07-28T19:18:59Z)
• On the Impact of PRB Load Uncertainty Forecasting for Sustainable Open RAN [2.526444902695476]
  The transition to sustainable Open Radio Access Network (O-RAN) architectures brings new challenges for resource management. We propose a novel approach to characterize the Physical Resource Block (PRB) load using probabilistic forecasting techniques.
  arXiv  Detail & Related papers  (2024-07-19T15:25:20Z)
• 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)
• Impact of data for forecasting on performance of model predictive control in buildings with smart energy storage [0.0]
  The impact on forecast accuracy of measures to improve model data efficiency are quantified. The use of more than 2 years of training data for load prediction models provided no significant improvement in forecast accuracy. Reused models and those trained with 3 months of data had on average 10% higher error than baseline, indicating that deploying MPC systems without prior data collection may be economic.
  arXiv  Detail & Related papers  (2024-02-19T21:01:11Z)
• ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [57.6987191099507]
  We introduce Exloss, a novel loss function that performs asymmetric optimization and highlights extreme values to obtain accurate extreme weather forecast. We also introduce ExBooster, which captures the uncertainty in prediction outcomes by employing multiple random samples. Our solution can achieve state-of-the-art performance in extreme weather prediction, while maintaining the overall forecast accuracy comparable to the top medium-range forecast models.
  arXiv  Detail & Related papers  (2024-02-02T10:34:13Z)
• DECODE: Data-driven Energy Consumption Prediction leveraging Historical Data and Environmental Factors in Buildings [1.2891210250935148]
  This paper introduces a Long Short-Term Memory (LSTM) model designed to forecast building energy consumption. The LSTM model provides accurate short, medium, and long-term energy predictions for residential and commercial buildings. It demonstrates exceptional prediction accuracy, boasting the highest R2 score of 0.97 and the most favorable mean absolute error (MAE) of 0.007.
  arXiv  Detail & Related papers  (2023-09-06T11:02:53Z)
• 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)
• Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems [87.4519172058185]
  An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
  arXiv  Detail & Related papers  (2020-02-20T04:58:07Z)

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.