Short time air temperature prediction using pattern approximate matching

Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems

“…The literature indicates that outdoor temperature prediction is crucial for more energyefficient HVAC systems in buildings, leading to the optimisation of their management and operation [1,[20][21][22][23]. This is because an accurate air-temperature prediction several hours ahead can provide the energy consumption and system load in advance.…”

“…Wang et al [22] developed and evaluated outdoor air-temperature prediction 3 h ahead based on an approximate pattern-matching algorithm (called TPAM) using historical meteorological data from five cities in China (Beijing, Harbin, Shanghai, Guangzhou, and Kunming) that represent different climatic zones. The parameters of the algorithm were defined for each city based on its historical data from January 2000 to December 2018 and then tested for 2019.…”

“…The research presented in this paper refers to the heating supply in a Polish building sector (multifamily building). The authors created prediction models of outdoor air temperature changes based on historical time series [1] using only one parameter (temperature) [22] combination as an input variable for the reasonable management of a boiler in a multifamily residential building during the heating season.…”

“…Papantoniou and Kolokotsa [26] forecasted temperature in four European cities (Ancona, Italy; Chania, Greece; Granada, Spain; and Mollet, Spain) with the use of neural networks with an MSE of up to 1.4 °C. Wang et al [22] attempted shortterm ambient-temperature prediction using pattern approximate matching obtained for five cities in China. The models were described by MSE values ranging from 1.2 to 9.5 °C.…”

“…The obtained results are consistent with those presented in the relevant literature. The values of SSE errors for the hourly data of the created models ranged from 4467 to 6199 (Table 1), which corresponds to MSE errors of 1.0-1.4 • C. Demirezen and Fung [21], who predicted outdoor air temperature with the use of neural networks for a cloud-based smart dual-fuel switching system using 2 min resolution data, obtained MSE in the range of 0.9-1.4 • C, while the accuracy of their prediction of hourly time series of temperature ranged from 1.0-5.1 • C. Papantoniou and Kolokotsa [26] forecasted temperature in four European cities (Ancona, Italy; Chania, Greece; Granada, Spain; and Mollet, Spain) with the use of neural networks with an MSE of up to 1.4 • C. Wang et al [22] attempted short-term ambient-temperature prediction using pattern approximate matching obtained for five cities in China. The models were described by MSE values ranging from 1.2 to 9.5 • C. Huang et al [41] modelled solar greenhouse air temperature using Laplace transform, with accuracy measured by MSE = 2.59 • C. Tran et al [42], who created medium air-temperature forecasts (several days ahead) using ANN, recurrent neural network (RNN), and long short-term memory (LSTM), obtained the best prediction results (average MSE = 7.4 • C) for LSTM.…”