This paper presents a methodology to develop archetype gray-box models and use them in an economic model-based predictive control algorithm to simulate optimal heating load management in response to a newly-introduced static time-of-use tariff for Québec's residential sector, rate Flex-D. The methodology is evaluated through a case study, wherein in situ measurements from a two-storey unoccupied research house of Hydro-Québec are used to develop an 11R6C network with a heuristic zoning-by-floor approach and compute the sequence of optimal electric heating input for the next control horizon. Properly-tuned economic model-based predictive control under rate Flex-D shows potential for an approximately 30% reduction in daily heating cost compared to the reference operation, with a minimal average deviation of indoor air temperature from the reference setpoint. Also, the analysis of the response's sensitivity to weather forecast uncertainties indicates that the most influential uncontrolled input directing the performance of economic model-based predictive control is the structure price signal, rendering the impact of uncertainty in the weather forecast negligible.

In this study, a copolymer composed of hollow spherical particles with an average particle size of 90 µm was evaluated as a lightweight aggregate in Portland cement-fly ash mortars to improve the thermal conductivity () of the composite. Mortars were produced for three different water/binder ratios by mass (), 0.4, 0.5 and 0.6. Optimized proportions were obtained for a minimum target compressive strength of 35 kg/cm (3.4 MPa) according to the requirements of Mexican standards for non-structural masonry units. Thermal conductivity was determined for dry and saturated samples through the transient plane technique with average results of 0.16 W/(m·K) and 0.31 W/(m·K), respectively. These values represent an increment of 23 % and a reduction of 33 %, respectively, in comparison to an efficient Portland cement-based commercially available thermal insulator.