Monthly average daily solar radiation simulation in northern KwaZulu-Natal: A physical approach

Betty Kibirige

doi:10.17159/sajs.2018/4452

Abstract

Solar energy is a poorly tapped energy source in northern KwaZulu-Natal (South Africa) and many locations in the region have no available measured solar radiation data. Unfortunately, these areas are among the rural, non-commercial farming areas in South Africa that need to harness solar radiation as an alternative energy source for their needs. These communities are mostly disadvantaged and unable to access the currently sophisticated approaches available for the prediction of such data. For this reason, a modelling tool accessible to these communities has been created using data from the South African Sugarcane Research Institute at eight stations in the region. This article presents the physical approach which can be used within readily available resources such as Microsoft Excel to develop a simulation environment that can predict monthly daily average solar radiation at locations. A preliminary model was later customised by considering the physical condition at each individual location. The validated tool provides estimations with a percentage root mean square error (%RMSE) of less than 1% for all locations except for Nkwaleni which had 1.645%. This is an extremely promising estimation process as compared to other methods that achieve estimations with %RMSE of above 10%. The simulation environment developed here is being extended to predict the performance of solar photovoltaic systems in the region. Using data from other sources, the approach is also being extended to other regions in South Africa. Significance:   The study modifies the physical approach that is deemed complicated to something that can be accessible to many communities. The accuracies achieved with this approach (<0.9%RMSE) in the considered region are commendable. The approach can be extended to other regions in South Africa.

Highlights

Northern KwaZulu-Natal is mostly rural with weather data collection stations widely spread apart
This study showed that the daily monthly average radiation received at any location will be affected by: latitude, altitude, day of the year, the earth terrain at the location and air mass (AM)
Starting with the solar constant, various mathematical expressions that represent the physical processes that the solar radiation encounters on its way to the earth’s surface were drawn upon to consolidate a complete model that provides an estimation of the daily monthly average solar radiation at a location within a region. This approach reduces the complications of the physical approach by including only equations that are significant to the modelling process

Summary

Introduction

Northern KwaZulu-Natal is mostly rural with weather data collection stations widely spread apart. Some researchers use satellite data to estimate the data at a location These approaches are rather sophisticated and expensive, making them inaccessible for rural communities. This article presents a physical approach using readily available resources like Microsoft Excel to develop an environment that can be used to predict monthly daily average solar radiation and eventually assist in photovoltaic system performance analysis. This approach is a consolidation of various reports on the intensity of solar radiation on the earth’s horizontal surface in an effort to come up with a non-abstract prediction of the same in northern KwaZulu-Natal. Four other stations were used to refine the preliminary model and the final two (with the lowest and the highest latitude) were used to validate the model

Methods

Results

Conclusion