Pseudo Dynamic Transitional Modeling of Building Heating Energy Demand Using Artificial Neural Network
This paper presents the building heating demand prediction model with occupancy profile and operational heating power level characteristics in short time horizon (a couple of days) using artificial neural network. In addition, novel pseudo dynamic transitional model is introduced, which consider time dependent attributes of operational power level characteristics and its effect in the overall model performance is outlined. Pseudo dynamic model is applied to a case study of French Institution building and compared its results with static and other pseudo dynamic neural network models. The results show the coefficients of correlation in static and pseudo dynamic neural network model of 0.82 and 0.89 (with energy consumption error of 0.02%) during the learning phase, and 0.61 and 0.85 during the prediction phase respectively. Further, orthogonal array design is applied to the pseudo dynamic model to check the schedule of occupancy profile and operational heating power level characteristics. The results show the new schedule and provide the robust design for pseudo dynamic model. Due to prediction in short time horizon, it finds application for Energy Services Company (ESCOs) to manage the heating load for dynamic control of heat production system.
Code (0)
등록된 구현이 없습니다.
Tasks
PredictionRobust DesignSimilar Papers 제목 키워드 기반
Economy and sustainability analysis with a novel modular configurable multi-modal white-box building model
This paper presents a novel modeling approach for building performance simulation, characterized as a white-box model with a high degree of modularity and flexibility, enabling direct integration into complex large-scale…
Deep Transfer Learning for Thermal Dynamics Modeling in Smart Buildings
Thermal dynamics modeling has been a critical issue in building heating, ventilation, and air-conditioning (HVAC) systems, which can significantly affect the control and maintenance strategies. Due to the uniqueness of e…
Domain AdaptationTransfer LearningForecasting Residential Heating and Electricity Demand with Scalable, High-Resolution, Open-Source Models
We present a novel framework for high-resolution forecasting of residential heating and electricity demand using probabilistic deep learning models. We focus specifically on providing hourly building-level electricity an…
Probabilistic Deep LearningDigital Twin for Grey Box modeling of Multistory residential building thermal dynamics
Buildings energy efficiency is a widely researched topic, which is rapidly gaining popularity due to rising environmental concerns and the need for energy independence. In Northern Europe heating energy alone accounts fo…
Cloud ComputingDecision MakingGenerating Building-Level Heat Demand Time Series by Combining Occupancy Simulations and Thermal Modeling
Despite various efforts, decarbonizing the heating sector remains a significant challenge. To tackle it by smart planning, the availability of highly resolved heating demand data is key. Several existing models provide h…
Time Series