Abstract
Thermostatic load control systems are widespread in many countries. Since they provide heat for domestic hot water and space heating on a massive scale in the residential sector, the assessment of their energy performance and the effect of different control strategies requires simplified modeling techniques demanding a small number of inputs and low computational resources. Data-driven techniques are envisaged as one of the best options to meet these constraints. This paper presents a novel methodology consisting of the combination of an optimization algorithm, two auto-regressive models and a control loop algorithm able to virtually replicate the control of thermostatically driven systems. This combined strategy includes all the thermostatically controlled modes governed by the set point temperature and enables automatic assessment of the energy consumption impact of multiple scenarios. The required inputs are limited to available historical readings from smart thermostats and external climate data sources. The methodology has been trained and validated with data sets coming from a selection of 11 smart thermostats, connected to gas boilers, placed in several households located in north-eastern Spain. Important conclusions of the research are that these techniques can estimate the temperature decay of households when the space heating is off as well as the energy consumption needed to reach the comfort conditions. The results of the research also show that estimated median energy savings of 18.1% and 36.5% can be achieved if the usual set point temperature schedule is lowered by 1 °C and 2 °C, respectively.
Highlights
In 2019, the final energy consumption of the residential sector accounted for 26% of the overall final energy consumption in the EU [1]
The dark-orange line corresponds to the indoor temperature gathered by the thermostat
The present research developed and validated a methodology to virtually emulate the performance of thermostatic load controlled systems relying on statistical learning models derived from the information gathered by smart thermostats
Summary
In 2019, the final energy consumption of the residential sector accounted for 26% of the overall final energy consumption in the EU [1]. Most EU Member States rely mainly on natural gas and electricity for meeting these needs, followed by renewable energies, mostly solid bio fuels. This high dependence on natural gas clearly determines any achievable strategy to reach the binding carbon targets. Many research studies have focused on demonstrating their cost effectiveness and how these technologies can increase the energy efficiency in several European countries [3,4,5,6,7] This strategy is the best option in the mid-long term. In the short term, cost-efficient strategies, able to drastically reduce the energy consumption of legacy space heating systems and, in particular, thermostatically driven systems (fed with gas), should be accelerated
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