• Energy Research
• 2021-2025close

Nowadays, the attention of designers and service providers is especially focused on energy efficiency and integration of renewable energy sources (RES). However, the knowledge on smart devices and automated, easily applicable algorithms for optimizing heating consumption by effectively taking advantage of solar heat gains, while avoiding overheating, is limited. This paper presents a simple method for taking into account the influence of solar heat gains in the form of solar radiation for the purposes of forecasting or controlling thermal power for heating of buildings. On the basis of field research carried out for seven buildings (five residential buildings and two public buildings) during one heating season, it was noticed that it was justified to properly narrow down the input data range included in the building energy model calculations in order to obtain a higher accuracy of calculations. In order to minimize the impact of other external factors (in particular wind speed) affecting the heat consumption for heating purposes, it was recommended to consider the data range only at wind speeds below 3 m/s. On the other hand, in order to minimize the impact of internal factors (in particular the impact of users), it was suggested to further narrow down the scope of the input data to an hour (e.g., 10–14 in multi-family residential buildings). During these hours, the impact on users was minimized as most of them were outside the building.

Abstract The energy efficiency of existing buildings may be increased by using new control techniques of their heating systems, especially if such methods are validated and easy to install. Hence, short-term forecasting of heat power demand is needed, in order to optimize their operation. This work presents a simple, new method of short-term forecasting of heat power for space heating, which may be easily applied in existing buildings. The method is first presented and then validated with two case studies, a multifamily building and a school, using hourly data from three heating seasons. It was found that beyond the outdoor meteorological parameters the accuracy of the method is improved by including the equivalent indoor temperature as the parameter related to the effect of the building occupant behavior. Accordingly, the resulting mean absolute percentage error of the predicted heat demand using the proposed prediction method was 3.2% and 12.0% for the two buildings. Compared to a simple model of the heat poser demand that is based only on the outdoor temperature error was lower by 61.4% and 43.2% for two buildings respectively. In addition, five profiles of equivalent indoor temperature were proposed in order to select the most accurate one for a specific building. This method may be also used in the process of predictive control of heating systems, because the external and internal parameters are measurable and predictable, which will contribute to more energy efficient systems in existing and new buildings.

The accurate prediction of heat demand in retrofitted residential buildings is crucial for optimizing energy consumption, minimizing unnecessary losses, and ensuring the efficient operation of heating systems, thereby contributing to significant energy savings and sustainability. Within the framework of this article, the dependence of the energy consumption of a thermo-modernized building on a chosen set of climatic factors has been meticulously analyzed. Polynomial fitting functions were derived to describe these dependencies. Subsequent analyses focused on predicting heating demand using artificial neural networks (ANN) were adopted by incorporating a comprehensive set of climatic data such as outdoor temperature; humidity and enthalpy of outdoor air; wind speed, gusts, and direction; direct, diffuse, and total radiation; the amount of precipitation, the height of the boundary layer, and weather forecasts up to 6 h ahead. Two types of networks were analyzed: with and without temperature forecast. The study highlights the strong influence of outdoor air temperature and enthalpy on heating energy demand, effectively modeled by third-degree polynomial functions with R2 values of 0.7443 and 0.6711. Insolation (0–800 W/m2) and wind speeds (0–40 km/h) significantly impact energy demand, while wind direction is statistically insignificant. ANN demonstrates high accuracy in predicting heat demand for retrofitted buildings, with R2 values of 0.8967 (without temperature forecasts) and 0.8968 (with forecasts), indicating minimal performance gain from the forecasted data. Sensitivity analysis reveals outdoor temperature, solar radiation, and enthalpy of outdoor air as critical inputs.

Abstract Smart control of energy supply to the existing buildings may increase their energy efficiency. However, to the best of the authors’ knowledge, there are no simple, general, automated, widely applicable and accurate methods for the creation of energy model of the building, which may be used to calculate the actual energy consumption of a heating system or for their prediction. This work presents a new simplified method for generating the energy characteristics of buildings and their heating systems, without the influence of occupants. The method requires as input only the actual heat supplied to the heating system and the local outdoor weather conditions (i.e. temperature, wind speed and solar insolation) of a building. The output is a building energy model in terms of an equivalent outdoor temperature. It was found that when determining the correction due to the wind, the data from the night hours (e.g. from 11.00 p.m. to 4.00 a.m.) should be used in order to exclude the impact of solar radiation and minimize the interaction of users. On the other hand, the correction due to the influence of solar radiation should be obtained using data with low wind speeds and time periods from 10.00 a.m. to 2.00 p.m. on weekdays for residential buildings or from 10.00 a.m. to 2.00 p.m. on the weekend for public buildings in order to minimize disruptive effects of wind speed and the impacts from occupants. This method may be used to generate a simple building energy model and to accurately determine the duration and the amount of heat power supplied to a building for space heating, for periods when the impact of occupants and other internal heat gains are kept to a minimum.