A novel approach to degree-hour calculation: Indoor and outdoor reference temperature based degree-hour calculation

doi:10.1016/j.energy.2010.02.038

Energy

Volume 35, Issue 6, June 2010, Pages 2455-2460

https://doi.org/10.1016/j.energy.2010.02.038 Get rights and content

Abstract

This paper presents a novel approach to temperature probability density distribution and function. Probability density functions and frequency are successfully used in wind speed and solar energy analyses in literature. This study applies these data to temperature data analysis. The present model is developed using the indoor and outdoor temperature as a parameter. Outdoor temperature distribution is crucial for the calculation of monthly and total degree-hour. In this paper, using past weather data, the outdoor temperature probability density functions are modeled for four cities in different regions in Turkey via a new computer program. The main advantage of this approach is to allow us to determine heating and cooling loads with respect to different indoor and outdoor temperatures.

Introduction

Degree-day and degree-hour values are commonly used for energy analyses of buildings. Many researchers have analyzed outdoor temperatures and published degree-day [1], [2], [3], [4], [5], [6] and degree-hour [7], [8], [9], [10], [11] values for many countries. It is known that degree-hour values are calculated simply by summing up the differences between the hourly dry-bulb temperatures and a standard reference temperature (base temperature). Instead of hourly outdoor temperature, daily average outdoor temperature is used in degree-day calculations. Cooling degree-hour values should be calculated based on hourly outdoor temperatures rather than daily average outdoor temperature to achieve more accurate results. It is observed that error rate increases when the daily average temperature is used. To understand the accuracy of daily average temperature base and hourly average temperature base, a sample calculation was performed for Istanbul in August (Fig. 1). As it can be seen from Fig. 1, calculating degree-hours through daily average temperature does not exhibit accurate results. For that reason, hourly based temperatures should be used during the calculation of degree-hour values. To determine heating and cooling degree-hour, two main factors should be known: outdoor temperature distribution and reference (base) temperature. These parameters directly affect the heating or cooling load. Reference temperatures for heating in building applications vary from country to country. For instance, in the UK, heating degree-hour values are based on an outside dry-bulb temperature of 15.5 °C, while Australia uses 18 °C and the United States uses 18.3 °C. Degree-hour values are used not only in building energy analyses but also industrial applications such as cold storage in cold chamber. Different indoor temperatures are required for cold storage depending on the food. Therefore it is not practical to calculate degree-hour values for each indoor temperature value. Generally, degree-hour values are calculated for constant base temperature. Only a few studies [9], [11] focused on both constant and variable base temperatures can be found in literature. It is clear that each month has different outdoor temperature and cooling degree-hours values. We should know probability density distribution and function to predict monthly cooling loads and profiles. Probability density functions and frequency, such as Weibull and Rayleigh, are successfully used in wind and solar energy analyses and it is commonly preferred by many researchers [12], [13], [14], [15], [16] in that context. In literature, there is no study showing temperature probability distribution and frequency as a parameter of outdoor temperature. In this study, a new approach is proposed for the estimation of outdoor probability density distribution. We calculated both temperature probability distribution and time elapsed in a month for any temperature interval of 1 °C. In the calculation, hourly outdoor temperature data of last 31 years recorded by the Turkish State Meteorological Service is used. Also given temperature data is compared with data recorded in our university for the last two years. Solar, temperature and wind data have been recorded to be used in the university project for last two years. A new computer program was generated for this analysis purposes by using Visual Basic data base.

Section snippets

Program description

The program was written by using Visual Basing data base. Program user interface and options are shown in Fig. 2. Input weather data can be loaded into program using a text file. After submitting weather data, program arranges data according to months and hours. Then, a temperature probability frequency is calculated for 1 °C interval. The program performs probability calculations with respect to time elapsed for each outdoor temperature. After that, it applies some suitable mathematical models

Case study

In order to illustrate how to use Eqs. (1), (2), (3), (4), (5), (6) effectively, the new approach proposed in this study is applied to one case study and Diyarbakir was chosen for this purpose. Model parameters and temperature limits are taken from Table 1. Eq. (1) is used to find the time elapsed for 1 °C temperature intervals.

Time elapsed between 0 and 1 °C was found by using 0.5 °C (average value) in the Eq. (1) as below. $H_{0.5} = 32.6374 + 32.5998 \cdot Cos (0.2815 (0.5) - 0.65) = 65.24 hour$

Time probability

Conclusions

The aim of this paper is to formulate temperature probability density distribution. This is the first approach in literature utilizing probability density distribution for temperature data analysis. Main advantages of the new approach can be presented as follows:

•
Heating and cooling degree-hour values or distribution can be calculated for each month with respect to any chosen base temperature.
•
User can easily calculate time elapsed in a month for temperatures below or above any chosen outdoor

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A novel approach to degree-hour calculation: Indoor and outdoor reference temperature based degree-hour calculation

Abstract

Introduction

Section snippets

Program description

Case study

Conclusions

Energy

Energy

Renewable Energy

Energy Conversion and Management

Energy

Energy Conversion and Management

Energy Conversion and Management

Solar Energy