| Category | EGCH | P07 | Determining light-source location using Machine Learning and |
| Solar Cells |
| Abstract | BACKGROUND |
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| The current generated by a single solar cell is highly sensitive to the |
| angle, the intensity, and the color of the light incident upon it. In this |
| project, I want to investigate whether this property can be utilized to |
| design an “intelligent” solar panel that can “predict” the position of a |
| light source incident upon it (and, possibly, also its distance and its |
| color). A potential application of such an “intelligent” panel would be as |
| a solar tracker, facilitating the determination of the position of the Sun |
| and feeding positional data to the tilting control unit. |
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| HYPOTHESIS |
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| My hypothesis is that if an array of solar cells can be properly |
| designed, then the electrical response of each cell in the array will be |
| different for different locations of an incandescent source of light |
| pointed towards it. These electrical response values can then be read |
| by a computer using proper sensors. The latter data, along with the |
| positional information of the light source can then be used to train a |
| supervised Machine Learning algorithm. The generated model can then |
| be used to predict the position of any incandescent light source |
| pointed at the array. |
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| EXPERIMENT: |
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| 1.Use Ruthenizer-535 synthetic dye to create dye-sensitized solar cells |
| (DSSCs). I choose to use DSSCs because I can produce them at home. |
| Moreover, DSSCs are environmentally friendly, cheaper to |
| manufacture than their silicon-based counterparts, and work in low-light |
| conditions. |
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| 2.Use 6-9 such DSSCs to create an array so that each cell responds |
| differently to an incandescent light source pointed at the array. |
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| 3.Connect each cell to the ADC input of an Arduino. |
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| 4.Read the voltage-drop on a 560-ohm resistor connected to each cell |
| using an Arduino program. |
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| 5.Record the position of the light source in a 2-dimensional / 3- |
| dimensional unit. |
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| 6.Use MatLab programs to train a Machine Learning algorithm and |
| generate a model for light-source-position prediction. |
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