Pseudo Dynamic Transitional Modeling of Building Heating Energy Demand Using Artificial Neural Network

TL;DR

This paper introduces a pseudo dynamic neural network model for short-term building heating demand prediction, incorporating occupancy and operational power levels, demonstrating improved accuracy and robustness in a case study.

Contribution

The paper presents a novel pseudo dynamic neural network model that considers time-dependent operational attributes for building heating demand prediction.

Findings

Correlation coefficient of 0.89 during learning

Prediction correlation coefficient of 0.85

Energy consumption error of 0.02%

Abstract

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.