Abstract
An accurate air-temperature prediction can provide the energy consumption and system load in advance, both of which are crucial in HVAC (heating, ventilation, air conditioning) system operation optimisation as a way of reducing energy losses, operating costs, as well as pollution and dust emissions while maintaining residents’ thermal comfort. This article presents the results of an outdoor air-temperature time-series prediction for a multifamily building with the use of artificial neural networks during the heating period (October–May). The aim of the research was to analyse in detail the created neural models with a view to select the best combination of predictors and the optimal number of neurons in a hidden layer. To meet that task, the Akaike information criterion was used. The most accurate results were obtained by MLP 3-3-1 (r = 0.986, AIC = 1300.098, SSE = 4467.109), with the ambient-air-temperature time series observed 1, 2, and 24 h before the prognostic temperature as predictors. The AIC proved to be a useful method for the optimum model selection in a machine-learning modelling. What is more, neural network models provide the most accurate prediction, when compared with LR and SVR. Additionally, the obtained temperature predictions were used in HVAC applications: entering-water temperature and indoor temperature modelling.
Highlights
The first stage of research into outdoor temperature prediction consists of the evaluation of how the number of model inputs, understood as the number of input variables, influences the prediction quality, as well as the determination of the best combination of input variables (Table 1)
Based on the Akaike information criterion (AIC), it was determined that the best ability to predict outdoor temperature was noted for the ANN of the following architecture: three input variables, three neurons in the hidden layer, one neuron in the output layer (MLP 3-3-1)
It was characterised by the highest SSE of 6199.361, AIC of 2223.594, and ξ of 0.201, which proves that in comparison to other models, the time series delayed by one hour contained a significantly lower amount of information necessary to create the prediction
Summary
The building sector is responsible for a major part of global energy consumption It uses about 40% of the generated power, which is more than that used in industry and transport [1,2]. There are several factors that influence the heating load, which include the energy-efficiency standard of the building, its heating capacity, and the position of the premises in relation to each other or to external walls. Another important aspect is the position of the building in terms of the directions of the world and the density of development in the surrounding area [5]. The so-called human factor that affects the internal heating-load balance has been discussed, among others, in the work of Stevenson and Leaman [8]
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