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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.

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This paper provides an overview of methods for testing the predictability of macroeconomic indicators based on real-time data revisions. Various approaches to their forecasting are considered and their properties are discussed.

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In this paper we compare two types of models for forecasting Russia’s GDP under the structural breaks. We consider models that allow breaks in a deterministic trend, in which the dates of structural breaks set exogenously, and more flexible class of models – with a stochastic trend. We show that models with a stochastic trend demonstrate the best result in GDP growth rates forecasting for a year ahead. For shorter horizons, the best forecasting model is the error correction model with a break in the deterministic trend in the GDP level.

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Time-varying parameter models have been widely spread in macroeconomic studies carried out over the past 20 years. Following the seminal papers in this field, various authors began to apply such models to analyze the time-varying behavior of such macroeconomic parameters as volatility, long-term economic growth, trend inflation, the persistence of inflation and oil prices, as well as the dependence of the main macroeconomic variables on oil prices. This review provides a methodological basis for using time-varying parameter models as one of the underlying tools of macroeconomic modeling and forecasting, as such models allow taking into account temporary changes in the relationships between different variables.

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