The CIE Standard General Sky divides the luminance distributions of skies into 15 types, ranging from overcast sky to clear sky. Finding the type of sky from the set of CIE Standard General Skies is a difficult task. This is due to the need for accurate luminance measurements over a long period of time because such data are not available in many regions around the world and to the difficulty in obtaining accurate readings for the zenith luminance. This study presents a method whereby the ratio of the vertical sky illuminance (or irradiance) on two surfaces or the ratio of vertical sky illuminance to the horizontal sky illuminance can be used to classify a sky into one of the CIE Standard General Skies.

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Saudi Arabia is considered one of the largest oil consumer countries. The country's contribution to global warming is continuously growing; local consumption of electrical energy is high and the demand is rapidly increasing. Based on the Kingdom's current energy status and its future plans, solar energy is shown as an attractive alternative, ample, reliable and clean resource for sustainable development. This study, review and investigates the potential of implementing solar energy into the Saudi residential sector by optimizing the integration of PV technology into buildings. The study field is confined to the typical Saudi house. Different BIPV placement methods were proposed and investigated through varied cases. Every case was parametrically tested solo in site so its highest energy potential could be measured first, creating a comparative baseline for its potential within the neighborhood-tested scenarios. The resulting impact was measured and analyzed through the measures of energy generation and consumption.

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PurposeBuildings are major contributors to greenhouse gases (GHG) along the various stages of the building life cycle. A range of tools have been utilised for estimating building energy use and environmental impacts; these are time-consuming and require massive data that are not necessarily available during early design stages. Therefore, this study aimed to develop an Environmental Impacts Cost Assessment Model (EICAM) that quantifies both energy and environmental costs for residential buildings.Design/methodology/approachAn Artificial Neural Network (ANN) was employed to develop the EICAM. The model consists of six input parameters, including wall type, roof type, glazing type, window to wall ratio (WWR), shading device and building orientation. In addition, the model calculates four measures: annual energy cost, operational carbon over 20 years, envelope embodied carbon and total carbon per square metre. The ANN architecture is 6:13:4:4, where the conjugate gradient algorithm was applied to train the model and minimise the mean squared error (MSE). Furthermore, regression analysis for the ANN prediction for each output was performed.FindingsThe MSE was minimised to 0.016 while training the model. Also, the correlation between each ANN output and the actual output was very strong, with an R2 value for each output of almost 0.998. Moreover, validation was conducted for each output, with the error percentages calculated at 0.26%, 0.25%, 0.03% and 0.27% for the annual energy cost, operational carbon, envelope materials embodied carbon and total carbon per square metre, respectively. Accordingly, the EICAM contributes to enhancing design decision-making concerning energy consumption and carbon emissions in the early design stages.Research limitations/implicationsThis study provides theoretical implications to the domain of building environmental impact assessment through illustrating a systematic approach for developing an energy-based prediction model that generates four environmental-oriented outputs, namely energy cost, operational energy carbon, envelope embodied carbon, and total carbon. The model developed has practical implications for the architectural/engineering (A/E) industries by providing a useful tool to easily predict environmental impact costs during the early design phase. This would enable designers in Saudi Arabia to make effective design decisions that would increase sustainability in the building life cycle.Originality/valueBy providing a holistic predictive model entitled EICAM, this study endeavours to bridge the gap between energy costs and environmental impacts in a predictive model for Saudi residential units. The novelty of this model is that it is an alternative tool that quantifies both energy cost, as well as building’s environmental impact, in one model by using a machine learning approach. Besides, EICAM predicts its outcomes more quickly than conventional tools such as DesignBuilder and is reliable for predicting accurate environmental impact costs during early design stages.

The effective use of daylight is a function of the luminance of the sky exposed to the glazing system. Therefore, accurate data about the luminance distribution of the sky are necessary for the optimum use of daylight. This paper compares seven models for estimating the angular sky luminance distribution. They were selected based on the ability to be used with all sky conditions and to determine the luminance of the sky from solar radiation. Measurements of solar radiation, sky luminance, and sky radiance were taken in a “maritime desert region” in Saudi Arabia. The results showed that the “Perez 93” model performed better than the other models tested, but there is a need for more studies to identify more accurate models for use in similar climatic conditions.

Optimum use of natural light in buildings requires knowledge of the luminance distribution of the sky. Since the introduction of the CIE Standard General Skies, several methods have been proposed to find the frequency distribution of sky types. This paper presents a method whereby the ratio of the luminances of two sky elements and the ratio of the luminance of one sky element to the horizontal illuminance can be used in the determination of sky type.