Assignment 2: An Overview on Solar Power Input and Estimating

Estimating Solar Panel Output With Open Source Data

August 31, 2022

Article Summary

Pre Information

Information needed to determine rooftop solar energy production
Roof slope
roof azimuth
number of solar panels roof can hold
shading factor
solar irradiance

Validation Data

We trust this methodology when we can: * Verify the derived inputs * Verify the final solar panel output Real validation data requires meter readings and gathering the data from those meters. You can also rely on estimations for organisations.

Sourcing Data

Different methods of data sourcing
Digital Surface Model (DSM)
Digital Terrain Model (DTM)
Building Footprint
Solar Irradiance

• DSM and DTM from LIDAR data: a method of measuring elevation by using aircrafts send out pulses of lasers and the time it takes for lasers to return provides the distance.
• Building Footprint from Open Street Map: Helps identify the shames from DSM and DTM and stores information of the geographic attributes
• Solar Irradiance Data:

1. Direct Normal Irradiance
2. Diffuse Horizontal Irradiation

Roof Segmentation

• Look at roofs in a collection

Estimating Shading

We can still get a reasonably accurate amount of shade cast on a roof using the following methods

Pseudo-DSM

• Shading can come from other taller objects
• You can compute DSM using building height values and footprint of surrounding buildings

UMEP: Solar Radiation: Shadow Generator

• UMEP stands for Urban Multi-scale Universal Predictor
• The Shadow Generator is a model of shadows at different times

Estimating Solar Panel Output

• Final solar output combines all of the information to answer questions of efficiency and capacity
• To simulate the performance you can use pvlib

Reliability Relies On

Questions for Reliability
When was the data alst updated?
Who collected the data?
Were any checks involved in ensuring the data is reliable?
Which houses might have been left out from data collection?

Related Links

Similar Articles on Solar Power

1. Predicting Solar Power Output Using Machine Learning Techniques
2. Solar Panel Power Generation Analysis
3. Building LSTM-Based Model for Solar Energy Forecasting

A Useful Article

I found this article really helpful in understanding the different ways solar power can be affected and measured. It is not as simple as just having sun exposure, but depending on the altitude and the surrounding area, the levels of solar power collected could differ. It was also interesting to learn how solar panels collect energy and how much energy can be generated depending on an area. The article does a good job simplifying subject matter terms in layman’s terms. It also provided a lot of good graphics and pictures that broke down processes and the science behind solar panels.

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