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Mean Bias Error (mean + bias_error)

Distribution by Scientific Domains

Engineering	50%

Selected Abstracts

Missing data estimation for 1,6,h gaps in energy use and weather data using different statistical methods

INTERNATIONAL JOURNAL OF ENERGY RESEARCH, Issue 13 2006
David E. Claridge
Abstract Analysing hourly energy use to determine retrofit savings or diagnose system problems frequently requires rehabilitation of short periods of missing data. This paper evaluates four methods for rehabilitating short periods of missing data. Single variable regression, polynomial models, Lagrange interpolation, and linear interpolation models are developed, demonstrated, and used to fill 1,6,h gaps in weather data, heating data and cooling data for commercial buildings. The methodology for comparing the performance of the four different methods for filling data gaps uses 11 1-year data sets to develop different models and fill over 500 000 ,pseudo-gaps' 1,6,h in length for each model. These pseudo-gaps are created within each data set by assuming data is missing, then these gaps are filled and the ,filled' values compared with the measured values. Comparisons are made using four statistical parameters: mean bias error (MBE), root mean square error, sum of the absolute errors, and coefficient of variation of the sum of the absolute errors. Comparison based on frequency within specified error limits is also used. A linear interpolation model or a polynomial model with hour-of-day as the independent variable both fill 1,6 missing hours of cooling data, heating data or weather data, with accuracy clearly superior to the single variable linear regression model and to the Lagrange model. The linear interpolation model is the simplest and most convenient method, and generally showed superior performance to the polynomial model when evaluated using root mean square error, sum of the absolute errors, or frequency of filling within set error limits as criteria. The eighth-order polynomial model using time as the independent variable is a relatively simple, yet powerful approach that provided somewhat superior performance for filling heating data and cooling data if MBE is the criterion as is often the case when evaluating retrofit savings. Likewise, a tenth-order polynomial model provided the best performance when filling dew-point temperature data when MBE is the criterion. It is possible that the results would differ somewhat for other data sets, but the strength of the linear and polynomial models relative to the other models evaluated seems quite robust. Copyright © 2006 John Wiley & Sons, Ltd. [source]

Modelling of air drying of Hac,haliloglu-type apricots

JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, Issue 2 2006
Hakan Okyay Menges
Abstract In this study a laboratory dryer was used for the thin layer drying of sulfured and non-sulfured apricots. The moisture ratio values throughout the drying process were calculated by 14 different mathematical models, namely Newton, Page, modified Page, modified Page-II, Henderson and Pabis, logarithmic, two-term, two-term exponential, Wang and Singh, Thompson, diffusion approximation, modified Henderson and Papis, Verma et al. and Midilli et al. models. Root mean square error, reduced chi-square, mean bias error, adjusted R -square and modelling efficiency were used as statistical parameters to determine the most suitable model among them. According to the results, the Page model was chosen to explain the thin layer drying behaviour of sulfured and non-sulfured apricots. The effects of drying air temperature (T) and velocity (V) on the constants and coefficients of the best moisture ratio model were determined by multiple regression analysis. The moisture ratio (MR) could be predicted by the Page model equation MR = exp(,ktn) with constants and coefficients k = 0.470893 + 0.078775V and n = 0.017786 exp(0.051935T) for sulfured apricots and k = 4.578252 + 1.144643T and n = 0.888040 + 0.145559V for non-sulfured apricots. It is possible to predict the moisture content of the product with the generalised Page model incorporating the effects of drying air temperature and velocity on the model constants and coefficients in the ranges T = 70,80 °C and V = 1,3 m s,1. This developed model showed acceptable agreement with the experimental results, explained the drying behaviour of the product and could also be used for engineering applications. Copyright © 2005 Society of Chemical Industry [source]

Precipitable water vapour estimation on the basis of sky temperatures measured by a single-pixel IR detector and screen temperatures under clear skies

METEOROLOGICAL APPLICATIONS, Issue 3 2010
A. Maghrabi
Abstract Precipitable water vapour (PWV) is an important component of the atmosphere, but remains difficult to measure with adequate spatial and temporal resolution under all weather conditions. Over the last four decades several techniques and methods have been developed to measure PWV more accurately, but each carries limitations preventing its widespread use. This paper presents preliminary results of a simple method for inferring PWV from the air temperature and infrared (IR) sky temperature under clear skies. Sky temperatures are measured using a broadband, single-pixel IR radiometer. A parametric model of the physical relationship between these three quantities was created using PWV data derived from a GPS receiver. By inverting the model, PWV estimates can be obtained from new temperature measurements. The measurements were taken between October 2002 and July 2004 in a coastal region of South Australia. The method was found to predict PWV quite accurately, with a mean bias error (MBE) of only , 0.009 mm and a root mean square error (RMSE) of 2.311 mm. The model was also compared to a set of 120 radiosonde-derived PWV values, resulting in a MBE and RMSE of 0.262 and 2.601 mm respectively. These preliminary results show that the clear sky PWV can be estimated accurately from sky temperatures obtained using a simple IR detector. Future work will extend the method to different sky and weather conditions. Copyright © 2009 Royal Meteorological Society [source]

A new method for estimating insolation based on PV-module currents in a cluster of stand-alone solar systems

PROGRESS IN PHOTOVOLTAICS: RESEARCH & APPLICATIONS, Issue 5 2007
Frans Nieuwenhout
Abstract In order to evaluate the performance of solar home systems (SHSs), data on local insolation is a prerequisite. We present a new method to estimate insolation if direct measurements are unavailable. This method comprises estimation of daily irradiation by correlating photovoltaic (PV) module currents from a number of SHSs, located a few kilometres apart. The method was tested with a 3-year time series for nine SHS in a remote area in Indonesia. Verification with reference cell measurements over a 2-month period showed that our method could determine average daily irradiation with a mean bias error of 1·3%. Daily irradiation figures showed a standard error of 5%. The systematic error in this method is estimated to be around 10%. Especially if calibration with measurements during a short period is possible, the proposed method provides more accurate monthly insolation figures compared with the readily available satellite data from the NASA SSE database. An advantage of the proposed method over satellite data is that irradiation figures can be calculated on a daily basis, while the SSE database only provides monthly averages. It is concluded that the new method is a valuable tool to obtain information on insolation when long-term measurements are absent. Copyright © 2007 John Wiley & Sons, Ltd. [source]