In the current scenario of increasing demand for solar Photo-voltaic (PV) systems, the need to predict their feasibility and monitor performance is more than ever. Although PV systems are known for their reliability, they are not above the damaging effects of their surroundings. Various lossy phenomena affect overall plant performance. In this paper, several of such losses, namely thermal, soiling, module degradation and inverter clipping, are discussed. Algorithms to evaluate these losses are proposed which are data-driven and empirical in nature. This is done as an effort to leverage the analytical capabilities provided by the plant data. The paper also compares the estimated losses with those obtained using the PVsyst simulation. As the latter is an independent industrial standard, it helps in understanding the ground reality of PV performance and insights for better operational monitoring. These insights are of immense business value and are aimed at optimizing performance and thereby revenue. As part of our asset management, all the solar PV plant components have sensors whose measurements are sent to the servers on a real-time basis. This is incorporated into our analytics portal which is used for operations and monitoring. The data used for this study is time-series in nature with a temporal least count of 5 minutes (instantaneous values spaced every 5min throughout the period of data capture). The actual data and its list of parameters is dependent on solar plant capacity and design site. For the reference dataset, a grid-connected solar rooftop PV plant in India was studied and its loss parameters were estimated. The plant components are discussed in the prologue of the results section. Solar PV is such a technology which has been enjoying increasing demand and this market scenario is quite favourable for innovation in energy research. This paper hopes to not only introduce the context of PV losses but also tries to engage the motivation to adopt data-driven and empirical methodologies to understand modern systems. This approach is better in the sense that it only gets better at prediction as time goes by and there is more data. Industrial research such as the above work in critical analysis of PV systems not only helps identify possible limitations but also suggest room for improvement. Since energy generation and project cost are key towards maximizing revenue, these estimation models aimed at predicting PV losses are to be deemed indispensable. As with any estimation, there is no one unique way of hitting the bull’s eye that is to know the exact value. The algorithms proposed above are very much dependent on the quality and quantity of data. However, the comparison between losses estimated using plant data and standard simulation using energy modelling can act as feedback towards improving the design and maintenance of such PV systems.

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.