scholarly journals A Pedestrian Approach to Indoor Temperature Distribution Prediction of a Passive Solar Energy Efficient House

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Golden Makaka
Keyword(s):  
Prediction Model ◽  
Thermal Behaviour ◽  
Indoor Environment ◽  
Statistical Prediction ◽  
Design Stage ◽  
Indoor Temperature ◽  
Comfort Levels ◽  
Simulation Results ◽  
Passive Solar ◽  
Statistical Prediction Model

With the increase in energy consumption by buildings in keeping the indoor environment within the comfort levels and the ever increase of energy price there is need to design buildings that require minimal energy to keep the indoor environment within the comfort levels. There is need to predict the indoor temperature during the design stage. In this paper a statistical indoor temperature prediction model was developed. A passive solar house was constructed; thermal behaviour was simulated using ECOTECT and DOE computer software. The thermal behaviour of the house was monitored for a year. The indoor temperature was observed to be in the comfort level for 85% of the total time monitored. The simulation results were compared with the measured results and those from the prediction model. The statistical prediction model was found to agree (95%) with the measured results. Simulation results were observed to agree (96%) with the statistical prediction model. Modeled indoor temperature was most sensitive to the outdoor temperatures variations. The daily mean peak ones were found to be more pronounced in summer (5%) than in winter (4%). The developed model can be used to predict the instantaneous indoor temperature for a specific house design.

scholarly journals Development of a Dynamics-Based Statistical Prediction Model for the Changma Onset

2015 ◽  
Vol 28 (17) ◽  
pp. 6647-6666 ◽  
Author(s):  
Hae-li Park ◽  
Kyong-Hwan Seo ◽  
Jun-Hyeok Son
Keyword(s):  
Prediction Model ◽  
North Atlantic ◽  
Korean Peninsula ◽  
Statistical Prediction ◽  
Central Pacific ◽  
Anticyclonic Anomaly ◽  
The North ◽  
The North Atlantic ◽  
Statistical Prediction Model ◽  
The North Pacific

Abstract The timing of the changma onset has high impacts on the Korean Peninsula, yet its seasonal prediction remains a great challenge because the changma undergoes various influences from the tropics, subtropics, and midlatitudes. In this study, a dynamics-based statistical prediction model for the changma onset is proposed. This model utilizes three predictors of slowly varying sea surface temperature anomalies (SSTAs) over the northern tropical central Pacific, the North Atlantic, and the North Pacific occurring in the preceding spring season. SSTAs associated with each predictor persist until June and have an effect on the changma onset by inducing an anticyclonic anomaly to the southeast of the Korean Peninsula earlier than the climatological changma onset date. The persisting negative SSTAs over the northern tropical central Pacific and accompanying anomalous trade winds induce enhanced convection over the far-western tropical Pacific; in turn, these induce a cyclonic anomaly over the South China Sea and an anticyclonic anomaly southeast of the Korean Peninsula. The diabatic heating and cooling tendency related to the North Atlantic dipolar SSTAs induces downstream Rossby wave propagation in the upper troposphere, developing a barotropic anticyclonic anomaly to the south of the Korean Peninsula. A westerly wind anomaly at around 45°N resulting from the developing positive SSTAs over the North Pacific directly reduces the strength of the Okhotsk high and gives rise to an anticyclonic anomaly southeast of the Korean Peninsula. With the dynamics-based statistical prediction model, it is demonstrated that the changma onset has considerable predictability of r = 0.73 for the period from 1982 to 2014.

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