Abstract
The purpose of this work is to determine internal and external factors affecting the cooling energy demand of a building. During the research, the impact of weather conditions and the level of hotel occupancy on cooling energy, which is necessary to obtain indoor comfort conditions, was analyzed. The subject of research is energy consumption in the Turówka hotel located in Wieliczka (southern Poland). In the article, the designer of neural networks was used in the Statistica statistical package. To design the network, a widely used multilayer perceptron model with an algorithm with backward error propagation was used. Based on the collected input and output data, various multilayer perceptron (MLP) networks were tested to determine the relationship most accurately reflecting actual energy consumption. Based on the results obtained, factors that significantly affect the consumption of thermal energy in the building were determined, and a predictive energy demand model for the analyzed object was presented. The result of the work is a forecast of cooling energy demand, which is particularly important in a hotel facility. The prepared predictive model will enable proper energy management in the facility, which will lead to reduced consumption and thus costs related to facility operation.
Introduction
The advancement of civilization and the development of society increase the amount of time that a person spends indoors. Currently, in developed countries, people spend up to 80–90% of their lives in buildings, which is why it is so important to ensure proper conditions and high indoor air quality. In new buildings’ heating, ventilation and air-conditioning systems play a crucial role due to high user comfort requirements. However, air treatment processes are very expensive, while the removal of heated air in winter causes the process itself to be unprofitable. The main tasks of designers in recent times are to reduce heating and cooling energy consumption through the use of devices and systems with higher efficiency, and to reduce energy losses during energy distribution, as well as through proper management of systems. The forecasting of energy consumption in a building is particularly important in terms of planning, managing, and optimizing energy systems. Accurate and reliable heating and cooling energy forecasts for buildings can bring significant benefits to energy savings. The forecasting heating energy consumption is a difficult task due to numerous disturbances and deviations from observed trends. In the case of facilities such as a hotel, the demand for cooling and heating energy, in addition to meteorological factors, is determined by the hotel occupancy and user activity (the use of facilities on the premises, e.g., swimming pool, restaurant, conference room, etc.).
Building energy consumption prediction is crucial to appropriate energy management and, subsequently, to improve the energy efficiency of systems and performance of the buildings. In general, methods for estimating and modeling energy consumption could be divided into two groups: engineering and data-driven approaches. The first type uses physical and thermodynamic functions to evaluate the energy consumption of the building or system. A data-driven approach defines the relationship between energy consumption and identified factors based on historical data [1]. In recent years, artificial intelligence methods have become very popular. This technique is often applied to the prediction of energy consumption due to good accurate prediction results. Among the most popular data-driven prediction models using the empirical approach are artificial neural networks (ANNs) and support vector machines (SSM) [2].
Artificial neural networks consist of three types of layers: input (this collects data and passes them on), hidden (connections between neurons are searched for here, i.e., the learning process takes place) and output (this collects conclusions and analysis results). A neural network can consist of any number of layers. Unprocessed data goes into the first layer. Each subsequent layer receives data resulting from the processing of data in the previous layer. What the last layer produces is the socalled system output [3]. The simplified artificial neural network was proposed for the first time by McCulloch and Pitts in 1943 [4].