International Business Machines Corporation

Universiti Brunei Darussalam

Rashmi Mittal

Yogish Sabharwal

Thomas George

Vaibhav Saxena

Chandrasekar Radhakrishnan

Ashish Verma

Vijay Arya

Anthony Paul Praino

Amar Prakash Azad

Lloyd Alan Treinish

Saiful A Husain

Saurav Basu

James Peter Cipriani

Methods, systems, and computer program products for cognitively predicting dust deposition on solar photovoltaic modules are provided herein. A computer-implemented method includes deriving, with respect to solar photovoltaic modules, dust parameters from image data, and estimating, for a given future time at a current module orientation, an amount of surface area of the modules that will be covered by dust and a yield loss of the modules associated with dust coverage. The method also includes forecasting, for the given future time at each of one or more modified module orientations, an amount of surface area of the modules that will be covered by dust and a yield loss of the modules associated with dust coverage. Further, the method includes generating an instruction to change the orientation of at least one of the modules, and outputting the instruction to at least one actuation system associated with the modules.

Cognitively predicting dust deposition on solar photovoltaic modules

One embodiment provides a method for determining at least one recommended vehicular travel route, the method including collecting a plurality of travel route inputs from a plurality of sources, relative to a travel route comprising a starting point and an ending point; determining at least one travel route recommendation by (i) assessing the collected plurality of travel route inputs and (ii) taking in to consideration vehicle emission impact parameters; and tracking vehicle adherence. Other variants and embodiments are broadly contemplated herein.

Travel planning based on minimizing impact of vehicular emission

Methods, systems, and computer program products for suggesting sensor placements are provided herein. A method includes simulating the concentration of a pollutant over an area covered by a sensor network grid by applying models to an emission inventory corresponding to the area; computing any discrepancy between the simulated concentration and historical data; applying scaling vectors to the first version to generate scaled versions of the emission inventory; re-simulating the concentration of the pollutant by applying the models to the scaled versions, and computing any discrepancy between the re-simulated concentrations and the historical data; identifying each scaling vector that corresponds with a discrepancy within a pre-determined range; applying the identified scaling vectors to an emission inventory value of each of the grid blocks to generate scaled emission inventory values; determining the grid blocks having high variability within scaled emission inventory values; and suggesting a sensor placement at the determined grid blocks.

Suggesting sensor placements for improving emission inventory

Methods and arrangements for automating weather model configurations. Characteristics of a target geographical area are received, as are a plurality of existing weather model configurations which are implemented for undertaking weather modeling in other geographical areas. A subset of the existing weather model configurations is selected, the subset comprising configurations corresponding to geographical areas having characteristics similar to the characteristics of the target area. A weather model is run with respect to each configuration in the subset of existing weather model configurations. Based on the running of a weather model with respect to each configuration, an output set of weather model configurations is selected for undertaking weather modeling in the target area.

Automating weather model configurations

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New Delhi, India

Rashmi Mittal