In France, buildings account for a significant portion of the electricity consumption (around 68 %), due to an important use of electrical heating systems. This results in high peak load in winter and causes tensions on the production-consumption balance. In view of reducing such fluctuations, advanced control systems (including the Model Predictive Control framework) have been developed to shift heating load while maintaining indoor comfort and taking advantage of the building thermal mass. In this paper, a framework for developing optimisationbased control strategies to shift the heating load in buildings is introduced. The balanced truncation method and a time-continuous optimisation method were used to develop a real-time control of the heating power. These two methods are well suited for control problems and yield precise results. The novelty of the approach is to use reduced models derived from advanced building simulation software. A simulation case study demonstrates the controller performance in the synthesis of a predictive model-based optimal energy management strategy for a single-zone test building of the "INCAS" platform built in Le Bourget-du-Lac, France, by the National Solar Energy Institute (INES).The controller exhibits excellent performance, reaching between 6 and 13 % cost reduction, and can easily be applied in real-time.

Highlights A tool was developed to help real estate owners plan refurbishment  The tool identifies priorities in terms of actions and buildings to be retrofitted  It combines dynamic building energy simulations and multi-objective optimisation  It was applied to a social housing stock with a multi-year budget constraint  Priorities are set based on the identified optimal renovation schedules

Discrepancies between ex-ante energy performance assessment and actual consumption of buildings hinder the development of energy performance contracting (EPC). To address this issue, uncertainty integration in simulation as well as measurement and verification (M&V) strategies have been studied. In this article, we propose a methodology, combining detailed energy performance simulation and M&V anticipation. Statistical studies using Monte-Carlo analysis allow a guaranteed consumption limit to be evaluated according to a given risk. Adjustment and verification procedures are also derived from the simulation results in relation to an optimised measurement plan. The complete process has been tested on a refurbishment project allowing the decrease of the difference between the guaranteed consumption limit and the reference energy consumption from 25 % to 15 % of reference consumption.

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