A novel approach of day-ahead cooling load prediction and optimal control for ice-based thermal energy storage (TES) system in commercial buildings

• URL: http://arxiv.org/abs/2509.13371v1
• Date: Tue, 16 Sep 2025 04:40:29 GMT
• Title: A novel approach of day-ahead cooling load prediction and optimal control for ice-based thermal energy storage (TES) system in commercial buildings
• Authors: Xuyuan Kang, Xiao Wang, Jingjing An, Da Yan,
• Abstract summary: Thermal energy storage (TES) is an effective method for load shifting and demand response in buildings.<n>This study proposed a novel integrated load prediction and optimized control approach for ice-based TES in commercial buildings.
• Score: 9.411623976714562
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Thermal energy storage (TES) is an effective method for load shifting and demand response in buildings. Optimal TES control and management are essential to improve the performance of the cooling system. Most existing TES systems operate on a fixed schedule, which cannot take full advantage of its load shifting capability, and requires extensive investigation and optimization. This study proposed a novel integrated load prediction and optimized control approach for ice-based TES in commercial buildings. A cooling load prediction model was developed and a mid-day modification mechanism was introduced into the prediction model to improve the accuracy. Based on the predictions, a rule-based control strategy was proposed according to the time-of-use tariff; the mid-day control adjustment mechanism was introduced in accordance with the mid-day prediction modifications. The proposed approach was applied in the ice-based TES system of a commercial complex in Beijing, and achieved a mean absolute error (MAE) of 389 kW and coefficient of variance of MAE of 12.5%. The integrated prediction-based control strategy achieved an energy cost saving rate of 9.9%. The proposed model was deployed in the realistic building automation system of the case building and significantly improved the efficiency and automation of the cooling system.

Related papers

• Partially stochastic deep learning with uncertainty quantification for model predictive heating control [0.0]
  We propose a partially deep learning (DL) architecture for building-specific indoor temperature modeling.<n>DL architecture shows significant potential to improve thermal comfort and energy efficiency in heating MPC solutions.<n>This work advances heating MPC, particularly for buildings with comprehensive datasets on their thermal behavior under various weather conditions.
  arXiv  Detail & Related papers  (2025-04-04T11:07:23Z)
• Feature Engineering Approach to Building Load Prediction: A Case Study for Commercial Building Chiller Plant Optimization in Tropical Weather [1.9182246029051933]
  This study presents a cooling load prediction model that combines a neural network with Kalman filtering and K-means clustering.<n> Applied to real world data from a commercial skyscraper in Singapore's central business district, the model achieved a 46.5% improvement in prediction accuracy.
  arXiv  Detail & Related papers  (2025-02-17T10:22:43Z)
• Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
  We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
  arXiv  Detail & Related papers  (2024-09-11T19:31:56Z)
• 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)
• Efficient mid-term forecasting of hourly electricity load using generalized additive models [0.0]
  We propose a novel forecasting method using Generalized Additive Models (GAMs) built from interpretable P-splines.<n>The proposed model is evaluated using load data from 24 European countries over more than 9 years.
  arXiv  Detail & Related papers  (2024-05-27T11:41:41Z)
• Improving Building Temperature Forecasting: A Data-driven Approach with System Scenario Clustering [3.2114754609864695]
  Heat, Ventilation and Air Conditioning systems cost approximately 40% of primary energy usage in the building sector. For large-scale HVAC system management, it is difficult to construct a detailed model for each subsystem. New data-driven room temperature prediction model is proposed based on the k-means clustering method.
  arXiv  Detail & Related papers  (2024-02-21T09:04:45Z)
• Global Transformer Architecture for Indoor Room Temperature Forecasting [49.32130498861987]
  This work presents a global Transformer architecture for indoor temperature forecasting in multi-room buildings. It aims at optimizing energy consumption and reducing greenhouse gas emissions associated with HVAC systems. Notably, this study is the first to apply a Transformer architecture for indoor temperature forecasting in multi-room buildings.
  arXiv  Detail & Related papers  (2023-10-31T14:09:32Z)
• A Hybrid Model for Forecasting Short-Term Electricity Demand [59.372588316558826]
  Currently the UK Electric market is guided by load (demand) forecasts published every thirty minutes by the regulator. We present HYENA: a hybrid predictive model that combines feature engineering (selection of the candidate predictor features), mobile-window predictors and LSTM encoder-decoders.
  arXiv  Detail & Related papers  (2022-05-20T22:13:25Z)
• Adaptive Control and Regret Minimization in Linear Quadratic Gaussian (LQG) Setting [91.43582419264763]
  We propose LqgOpt, a novel reinforcement learning algorithm based on the principle of optimism in the face of uncertainty. LqgOpt efficiently explores the system dynamics, estimates the model parameters up to their confidence interval, and deploys the controller of the most optimistic model.
  arXiv  Detail & Related papers  (2020-03-12T19:56:38Z)
• A latent variable approach to heat load prediction in thermal grids [10.973034520723957]
  The method is applied to a single multi-dwelling building in Lulea, Sweden. Results are compared with predictions using an artificial neural network.
  arXiv  Detail & Related papers  (2020-02-13T09:21:17Z)
• NeurOpt: Neural network based optimization for building energy management and climate control [58.06411999767069]
  We propose a data-driven control algorithm based on neural networks to reduce this cost of model identification. We validate our learning and control algorithms on a two-story building with ten independently controlled zones, located in Italy.
  arXiv  Detail & Related papers  (2020-01-22T00:51:03Z)

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.