Predictive Optimization of the Heat Demand in Buildings at the City Level

Petri Hietaharju,Mika Ruusunen,Kauko Leiviskä,Marko Paavola

doi:10.3390/app9101994

Abstract

Easily adaptable indoor temperature and heat demand models were applied in the predictive optimization of the heat demand at the city level to improve energy efficiency in heating. Real measured district heating data from 201 large buildings, including apartment buildings, schools and commercial, public, and office buildings, was utilized. Indoor temperature and heat demand of all 201 individual buildings were modelled and the models were applied in the optimization utilizing two different optimization strategies. Results demonstrate that the applied modelling approach enables the utilization of buildings as short-term heat storages in the optimization of the heat demand leading to significant improvements in energy efficiency both at the city level and in individual buildings.

Highlights

According to a recent study by Connolly [1], heating in buildings presents the largest energy demand in 15 EU countries, and it is higher than cooling demand in all 28 EU countries
Indoor temperature was subject to bounding constraint in Equation (6) inflicting penalty (TPenalty) if for minimum variance optimization, the total heat demand for the 48 h was subject to inequality breached
For peak load demand was subject to the inequality constraint in Equation (8) inflicting a penalty (PchangePenalty ) if cutting the hourly heat demand was subject to the inequality constraint in Equation (8) inflicting a breached

Summary

Introduction

According to a recent study by Connolly [1], heating in buildings presents the largest energy demand in 15 EU countries, and it is higher than cooling demand in all 28 EU countries. Peak loads occur when the heat demand exceeds the capacity of the power plants. Oil powered reserve capacity is typically needed. This raises production costs and environmental impact for the energy company and creates a strong economic and environmental incentive to find ways to cut peak loads and reduce the use of oil. This article presents simulation studies employing previously developed, adaptable indoor temperature and heat demand models to demonstrate their applicability to predictively optimize the heat demand and to cut the peak loads by utilizing buildings as short-term heat storage. The applied modelling approach is argued to provide a cost-effective way to implement predictive optimization and to improve energy efficiency in heating

Methods

Results

Conclusion