<div><div>In the current scenario of increasing demand for solar photovoltaic (PV) systems, the need to predict their feasibility and performance is more than ever. Irradiance of a geographical location almost exclusively determines the generation possible via solar. Hence, accurate irradiance data is required to assess the value of solar PV systems. Emphasizing such need, this paper presents a method of estimating global horizontal irradiance (GHI) using the two dimensional (2-D) spatial interpolation technique. The proposed model is geo-agnostic and can estimate irradiance depending on the geographical range of the input data. This paper also compares the model predictions with a standard irradiation dataset in the industry. This comparison helps in getting insights regarding the spatio-temporal trends in recent times. As part of our asset management, solar PV plants spread all over India have irradiation sensors whose measures are sent to our servers on a real-time basis. This is incorporated into our in-house analytics portal which is developed for operations and monitoring. Thus, the data is organized for each plant with its geographical parameters (latitude and longitude) along with Global Tilted Irradiation (GTI) measured by on ground sensors. T-factors (calculated as function of tilt, azimuth of the site) corresponding to each sensor orientation are also known which are used to obtain Global Horizontal Irradiation (GHI) values. As part of our study, the increasing predominance of solar PV as a renewable source of energy is discussed. This has focused the attention on the need to have quality irradiation data. The above research has been as an endeavour to use a data-driven approach to solve the issue at hand. Hopefully, this work can showcase the power of using data-intensive techniques such as the one shown to solve the many challenges in the energy industry especially those in solar. The model is built using irradiation sensor data pan India and used an effective spatial interpolation technique, kriging, to produce the gap-filled estimates. The statistical measures of estimate error are also mentioned which show impressive accuracy. Heat maps for respective months have also been produced for better visualization of GHI trends. An independent dataset of industrial benchmarking standards is also compared with the estimates to better understand the temporal GHI trends with respect to long-term averaged values. The assessment of this work’s potential is for the industrial community to ascertain as this can have various use cases of immense business value.</div></div>
Sensor Data Interpretation with Clustering for Interactive Asset-Management of Urban Systems