scholarly journals Modeling ambient air temperature time series using neural networks

1998 ◽  
Vol 103 (D16) ◽  
pp. 19509-19517 ◽  
Author(s):  
G. Mihalakakou ◽  
M. Santamouris ◽  
D. Asimakopoulos
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Air Temperature ◽  
Ambient Air ◽  
Temperature Time Series ◽  
Ambient Air Temperature ◽  
Temperature Time

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

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7512
Author(s):  
Joanna Kajewska-Szkudlarek ◽  
Jan Bylicki ◽  
Justyna Stańczyk ◽  
Paweł Licznar
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Time Series Prediction ◽  
Information Criterion ◽  
Ambient Air ◽  
Optimal Number ◽  
Temperature Time Series ◽  
Neural Network Models ◽  
Temperature Prediction ◽  
Temperature Time

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.

scholarly journals Non-stationary temporal characterization of the temperature profile of a soil exposed to frost in south-eastern Canada

2008 ◽  
Vol 15 (3) ◽  
pp. 409-416 ◽  
Author(s):  
F. Anctil ◽  
A. Pratte ◽  
L. E. Parent ◽  
M. A. Bolinder
Keyword(s):  
Time Series ◽  
Soil Temperature ◽  
Temperature Profile ◽  
Air Temperature ◽  
Soil Depth ◽  
Time Lag ◽  
Power Spectra ◽  
Temperature Time Series ◽  
Soil Temperatures ◽  
Temperature Time

Abstract. The objective of this work was to compare time and frequency fluctuations of air and soil temperatures (2-, 5-, 10-, 20- and 50-cm below the soil surface) using the continuous wavelet transform, with a particular emphasis on the daily cycle. The analysis of wavelet power spectra and cross power spectra provided detailed non-stationary accounts with respect to frequencies (or periods) and to time of the structure of the data and also of the relationships that exist between time series. For this particular application to the temperature profile of a soil exposed to frost, both the air temperature and the 2-cm depth soil temperature time series exhibited a dominant power peak at 1-d periodicity, prominent from spring to autumn. This feature was gradually damped as it propagated deeper into the soil and was weak for the 20-cm depth. Influence of the incoming solar radiation was also revealed in the wavelet power spectra analysis by a weaker intensity of the 1-d peak. The principal divergence between air and soil temperatures, besides damping, occurred in winter from the latent heat release associated to the freezing of the soil water and the insulation effect of snowpack that cease the dependence of the soil temperature to the air temperature. Attenuation and phase-shifting of the 1-d periodicity could be quantified through scale-averaged power spectra and time-lag estimations. Air temperature variance was only partly transferred to the 2-cm soil temperature time series and much less so to the 20-cm soil depth.

scholarly journals Uncertainty evaluation of Climatol ' s adjustment algorithm applied to daily air temperature time series

2020 ◽  
Author(s):  
Oleg Skrynyk ◽  
Enric Aguilar ◽  
Jose Guijarro ◽  
Luc Yannick Andreas Randriamarolaza ◽  
Sergiy Bubin
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Temperature Time Series ◽  
Uncertainty Evaluation ◽  
Temperature Time

scholarly journals Spatial and Temporal Distribution of Air Temperature in the Northern Hemisphere

2013 ◽  
Vol 6 (3) ◽  
pp. 177-182
Keyword(s):  
Time Series ◽  
20Th Century ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Temporal Distribution ◽  
Surface Air Temperature ◽  
Temperature Time Series ◽  
Mean Values ◽  
The Mean ◽  
Temperature Time

In the present study, the spatial and temporal surface air temperature variability for the Northern Hemisphere has been examined, for the period 1900-1996. Factor Analysis has been applied to 5o Latitude x 10o Longitude grid box data covering the area from almost the equator to 70o N. These data are anomalies of the mean annual air temperature from the respective mean values of the period 1961- 1990. The analysis showed that, mainly 20 regions were determined in the Northern Hemisphere with significantly covariant air temperature time series. The comparison of the trends of the mean annual surface air temperature time series of these regions, revealed such common characteristics as the minimum of the first decade of the 20th century and the recent years warming. The results of this study are also compared to the respective results of a former study in which data for the last half of the century (1948-1996) have been analyzed. The findings extracted indicate the stability of climate distribution in Northern Hemisphere during the 20th century.

Multi-Fractal Analy is of Daily Air Temperature Time Series in Coastal Areas of China

2020 ◽  
Vol 26 (3) ◽  
pp. 348-362
Author(s):  
LI Zhong-liang ◽  
HE Guang-xin ◽  
SHEN Wei-shou ◽  
ZHEN Xiao-ju ◽  
HAN Jing
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Coastal Areas ◽  
Temperature Time Series ◽  
Temperature Time

Time series analysis of ambient air-temperature during the period 1970–2016 over Sydney, Australia

2019 ◽  
Vol 648 ◽  
pp. 1627-1638 ◽  
Author(s):  
I. Livada ◽  
A. Synnefa ◽  
S. Haddad ◽  
R. Paolini ◽  
S. Garshasbi ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Air Temperature ◽  
Ambient Air ◽  
Ambient Air Temperature ◽  
Series Analysis ◽  
Sydney Australia
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