Due to an increasing demand for an improvement in environmental conditions for living and a need for energy saving, development of daylight exploitation products has accelerated. The invention of light-pipes which bring natural light indoors where sunlight cannot reach without generating excessive heat is one such example. Mathematical modelling activities aimed at predicting the daylighting performance achievable by light-pipes with various configurations under all weather conditions in the UK are being undertaken. Two models, one for straight light-pipes and the other for elbowed light-pipes are described. The models enable estimation of daylight provision of the light-pipes with a high degree of accuracy i.e., R² values of 0.95 and 0.97 for regression between predicted and measured illuminance were respectively obtained for the above models. The maximum Mean Bias Error (MBE) and Root Mean Square Error (RMSE) were -2 lux and 27 lux. A design guide for light-pipe assessment is presented that will obviate the need for use of mathematical models.

This paper presents the optimal sizing and life cycle assessment of residential photovoltaic (PV) energy systems. The system consists of PV modules as the main power producer, and lead–acid batteries as the medium of electricity storage, and other essential devices such as an inverter. Five‐parameter analytic PV cell model is used to calculate the energy production from the modules. Electrical needs for a family living under normal conditions of comfort are modelled and used within simulation of the system performance, with an average daily load of approximately 9·0 kWh. The system's performance simulations are carried out with typical yearly solar radiation and ambient temperature data from five different sites in Turkey. The typical years are selected from a total of 6 years data for each site. The life cycle cost of the PV system is analysed for various system configurations for a 20‐year system life. The role of the batteries in PV energy systems are analysed in terms of the cost and power loss. The system performance is analysed as a function of various parameters such as energy production and cost. It is shown that these change substantially for different system configurations and locations. The life cycle assessment of the energy system described was also carried out to determine the environmental impact. It was found that, with the conservative European average electricity mix, energy pay back time (EPBT) is 6·2 years and CO₂ pay back time is 4·6 years for the given system. Copyright © 2007 John Wiley & Sons, Ltd.

In the absence of ground reflected radiation measured data, an average albedo value of 0.2, which describes the reflective properties of the bare ground is generally used. The variation of albedo based on different foreground surfaces and under different atmospheric conditions is an area which is under research. This paper presents an experimental investigation of albedos of different foreground materials that can be used for photovoltaic (PV) applications. Overcast skies are predominant in northerly locations and will have a high frequency of low solar altitudes, and thus ground reflection is an important contributor towards total solar energy gain. The foreground surfaces include common materials: Grass, sand, and cement slabs, and some non-conventional materials: White pebbles, white boards, white tiles, and aluminium foil. The impact of factors, such as ageing, solar elevation, rain, and cloud cover (sky conditions) is analysed to determine the changes in albedos of these materials. Each material was observed to have individual performance characteristics under these factors. It was found that the non-conventional materials were least prone to weather-related changes and have higher albedo values as compared to the conventional materials, and also have good potential to replace the conventional materials for any given PV application.

Solar energy applications require readily available, site-oriented, and long-term solar data. However, the frequent unavailability of diffuse irradiation, in contrast to its need, has led to the evolution of various regression models to predict it from the more commonly available data. Estimating the diffuse component from global radiation is one such technique. The present work focuses on improvement in the accuracy of the models for predicting horizontal diffuse irradiation using hourly solar radiation database from nine sites across the globe. The influence of sunshine fraction, cloud cover, and air mass on estimation of diffuse radiation is investigated. Inclusion of these along with hourly clearness index, leads to the development of a series of models for each site. Estimated values of hourly diffuse radiation are compared with measured values in terms of error statistics and indicators like, $R2$, mean bias deviation, root mean square deviation, skewness, and kurtosis. A new method called “the accuracy score system” is devised to assess the effect on accuracy with subsequent addition of each parameter and increase in complexity of equation. After an extensive evaluation procedure, extricate but adequate models are recommended as optimum for each of the nine sites. These models were found to be site dependent but the model types were fairly consistent for neighboring stations or locations with similar climates. Also, this study reveals a significant improvement from the conventional $k-kt$ regression models to the presently proposed models.

Accurate solar radiation data are essential for the development of solar energy application systems. The limited availability of solar radiation data, and especially diffuse irradiance values, makes it vital to develop models to estimate these data. The development of estimation models has been the objective of many studies. This paper presents an extended review of the diffuse ratio (k) vs. clearness index (k_t) annual, monthly, daily, and hourly frequency regression models. It is however interesting to note that there is a dearth of such knowledge for diffuse ratio–clearness index regressions that are based on averaged data. Monthly-averaged daily global irradiation data are now easily available from the NASA website for any global location. Using existing models, it is possible to decompose the daily to averaged-hourly global irradiation values. The missing link so far has been hourly averaged diffuse irradiation. This article presents regression equations which could be used to estimate that information. For this purpose, hourly global and diffuse irradiation data was pooled from 19 different locations to obtain three latitude-dependent regression models relating the monthly-averaged hourly diffuse ratio ( $\overline{k}$ ) to the clearness index ( $\overline{k_t}$ ). The results show a high relationship between both variables. These regression equations could be used to estimate the averaged diffuse irradiation values from averaged global irradiation values, which are more easily available.