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New research tackles uncertainty in smart building load forecasting

By PulseAugur Editorial · [2 sources] ·

A new research paper explores probabilistic load forecasting for smart buildings, focusing on how to handle uncertainty introduced by reconstructed input features. The study compares a post-hoc residual-quantile method with an integrated in-model quantile-learning scheme using three deep learning backbones, including Temporal Fusion Transformers (TFT). Results indicate that the optimal placement of uncertainty is dependent on the model architecture, with integrated quantile learning proving most effective for the TFT model. AI

IMPACT This research could improve the accuracy and reliability of energy demand predictions in smart buildings, potentially leading to more efficient energy management and cost savings.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new methodology for probabilistic load forecasting.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research tackles uncertainty in smart building load forecasting

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The cluster contains a research paper published on arXiv detailing a new methodology for probabilistic load forecasting.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Sarah Al-Shareeda, Gulcihan Ozdemir, Heung Seok Jeon ·

    Learning-based Probabilistic Load Forecasting with Post-hoc and In-model Uncertainty

    arXiv:2607.12730v1 Announce Type: cross Abstract: Smart-building load forecasters are often trained offline on dense, multivariate, high-frequency data, but deployment may provide only hourly, feature-limited inputs. Missing features must then be reconstructed, and their errors c…

  2. arXiv cs.LG TIER_1 English(EN) · Heung Seok Jeon ·

    Learning-based Probabilistic Load Forecasting with Post-hoc and In-model Uncertainty

    Smart-building load forecasters are often trained offline on dense, multivariate, high-frequency data, but deployment may provide only hourly, feature-limited inputs. Missing features must then be reconstructed, and their errors can propagate through the model. If this input unce…

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