Bin Weather Data Research Articles

Study of climate change impacts on the lifespan of a bin weather data set in Senai, Malaysia

The present study presents the climate change impacts on bin weather data and bin method for the city of Senai, Malaysia. Seventeen case simulations were conducted to analyse its impacts on the results of the HVAC energy analysis and determine the suggested lifespan for the bin weather data of Senai. The graph of the mean temperatures has shown a significant increasing trend signalling the occurrence of climate change. Taking the year of 2001 as the baseline, the differences in the number of occurrence hours were grouped into two ranges, which are low temperature (18 °C to 28 °C) and high temperature (28 °C to 38 °C). In 2016, a significant 9.7% of temperature data for the year has switched from low temperature to high temperature. From the case simulations conducted, the annual energy consumption has shown an increasing trend. By using the case simulation of 2018 as the baseline, the degree of deviation ranges from 9.68% to 7.44%. The 10 years of lifespan are suggested at the end of the present study because 75% of the bin weather data beyond 10 years used in the case simulations have a percentage change above 5%.

Bin weather data of Varna and their influence on the seasonal efficiency of heat pumps with energy source outdoor air

The seasonal transformation coefficient (SCOP) of a heat pump with a heat source outside air as a function of the temperature of the latter is studied. The bin method was used to determine the climatic data for the city of Varna. Outdoor air temperature data were used and statistical analysis of the data was performed. The average frequency and cumulative distribution of temperature bins values in the period from 2005 to 2019 in the city of Varna is calculated. The results are presented using graphs and tables. In the data stability assessment report, the standard deviation and coefficient of variation were used. The SCOP of a heat pump heating installation operating in Varna has been calculated. All results were compared with those of other studies of the authors under different conditions and with reference data.

Application of the bin weather data for building energy analysis in the tropics

The present study presents the bin weather data for four cities in the tropical region and its significance in the building energy analysis. The bin weather data for four cities in Malaysia, which are Bayan Lepas, Kuala Terengganu, Senai and Kuching, were generated. The bin weather data are in the range from 18 to 38 °C with 2 °C intervals and tabulated based on six 4-h shifts. Since there is a lack of updated bin weather data in Malaysia, the present paper provides valuable data for engineers and designers to conduct an energy analysis using the bin method in this region. This data may also serve as an important reference for all tropical regions with similar weather characteristics. Energy analysis is an important tool for estimating the energy usage of a building in order to minimize energy wastage and maximize energy efficiency. The significance of applying bin weather data in energy analysis is also investigated using a case simulation. When the mean outdoor temperature of occupied period is used in simulation, the results deviate less than 1.6% from the results with the bin weather data, but the deviation rises up to 16.68% with the use of the annual mean outdoor dry bulb temperature.

A simple approach to estimate ambient temperature bin data

In this study, a simple methodology is proposed to estimate ambient temperature bin data. The proposed model is based on the determination of the best fitting equation describing the characteristics of the cumulative frequency distribution of yearly bin weather data values. This approach makes it easy for anyone, who needs bin data values for any location, to adopt the fitted equations in the building energy performance calculations. A case study was applied to six cities in Turkey, and the applicability of the proposed model has been shown. The obtained R2 values of the fitted equations are higher than 0.99. Therefore, the coefficients of the fitted equations for any location can be used easily to predict any bin data value to be used in building energy performance calculations. The results of this study are important for the experts using bin method.

Bin weather data for different climates of Turkey

In this study, bin weather data and bin method for Turkey which is a well-known and simple method to estimate heating and cooling loads were investigated. The bin weather data for dry-bulb temperature from -50°C to +50°C with 3°C increments were generated in six daily 4-h shifts. Typical meteorological year datasets were used to generate bin weather data for the selected eight cities (Istanbul, Ankara, Trabzon, Van, Diyarbakr, Adana, Sivas and Denizli) representing the distinct climatic zones of Turkey. Using typical meteorological year data in bin data generation is the original part of this study. Yearly total Nbin values for the selected cities were tabulated. Results of this study are important due to the data used, fill an important gap for Turkey about bin data and can simply be used in prediction of heating and cooling energy consumptions of buildings. Additionally, this study contributes well to the world literature on bin data.

Bin weather data for different climates of Turkey

Bin weather data for different climates of Turkey

Generation of ambient temperature bin data of 26 cities in China

There are many kinds of methods to estimate the energy requirement or fuel consumption in the optimal design and minimize the life cost of a building. The bin method is one of the most popular ways to estimate the energy performance of a building, which is based on the hourly-based outdoor dry-bulb temperatures of all days in a whole year. There is lack of information about the bin weather data in China. In this paper, the ambient temperature bin data for 26 cities in China are generated. Based on the method used in this paper, the bin data can be calculated with the long-term daily weather record or TMY weather data, the deviation between them is acceptable. The bin data given in this paper may have the positive impact on building energy conservation in China.

Bin weather data for the provinces of the Eastern Anatolia in Turkey

This paper presents information about bin weather data that have been generated from weather histories and recorded over a period of many years for 14 cities located in the Eastern Anatolia region in Turkey. The analysis covers 14 provinces: Ağrı, Ardahan, Bingöl, Bitlis, Elazığ, Erzincan, Erzurum, Hakkari, Iğdır, Kars, Malatya, Muş, Tunceli, and Van. The bin data for dry-bulb temperature from −36 °C to 45 °C with 3 °C increments are calculated in six daily 4-h shifts (1–4, 5–8, 9–12, 13–16, 17–20 and 21–24 h). In the calculations, the hourly dry-bulb temperatures recorded by the Turkish State Meteorological Affairs between the years 1990 and 2005 (16 years) are used. Monthly and yearly bin data are presented in tables and on graphs. The information and data presented in this paper hope to fill a gap in information needed by building designers and engineers for simplified energy calculations of buildings in the Eastern Anatolia region.

Bin weather data for 38 Greek cities

Ambient temperature bin data are used for estimating the energy consumption for heating and cooling of buildings. This well-known method is a steady-state approach and the energy requirements are determined at various outdoor temperatures, in order to account for the effect of outside temperature on the HVAC equipment efficiency. The application of the method requires detailed bin data. In this paper, the dry-bulb temperature bin data for 38 Greek cities are determined by using a reliable estimating methodology, based on monthly-average outdoor temperatures and solar clearness index. The data are calculated from −18 °C to 42 °C with 2 °C increments in six daily 4-h shifts, and are presented in tabular form.

A stochastic method to generate bin weather data in Nanjing, China

Abstract The bin method, as one of the well known and simple steady state methods used to predict heating and cooling energy consumption of buildings, requires reliable and detailed bin data. Since the long term hourly temperature records are not available in China, there is a lack of bin weather data for study and use. In order to keep the bin method practical in China, a stochastic model using only the daily maximum and minimum temperatures to generate bin weather data was established and tested by applying one year of measured hourly ambient temperature data in Nanjing, China. By comparison with the measured values, the bin weather data generated by the model shows adequate accuracy. This stochastic model can be used to estimate the bin weather data in areas, especially in China, where the long term hourly temperature records are missing or not available.

Bin weather data for Turkey

One of the well-known and simple steady-state methods used to predict heating and cooling energy consumption of buildings is the bin method that requires reliable and detailed bin data. In this study, the bin data for Turkey are determined by using long-term measured data. The bin data for dry-bulb temperature from −36 °C to 45 °C with 3 °C increments are calculated in six daily 4-h shifts for 78 provinces of Turkey.

Technical note Bin weather data of Athens, Greece

The necessary analysis of the ambient dry bulb temperature and of the relative humidity for elaboration of bin weather data is discussed. Using weather data from Athens, Greece, the annual total bin data as well as monthly bin data in four-hour periods are calculated and presented in tabular form. The presented data serve for the estimation of the energy requirements and fuel consumption of heating and air conditioning systems for either short or long time periods of operation.

Bin weather data of Thessaloniki, Greece

The necessary analysis of the ambient dry bulb temperature and of the relative humidity for elaboration of bin weather data is discussed. Using weather data from Thessaloniki, Greece, the annual total bin data as well as monthly bin data in 4-h periods are calculated and presented in tabular form. The presented data serve for the estimation of the energy requirements, and fuel consumption of heating and air conditioning systems for either short or long time periods of operation.