This post is about ‘When 20th-century electricity meets 21st-century electricity, i.e., Artificial Intelligence.
Andrew Ng has famously quoted.
Artificial Intelligence is the new ‘Electricity’. — Andrew Ng
Recently I came across an opportunity to apply machine learning techniques to the renewable energy industry. In the beginning, I was skeptical if ML (Machine Learning) could do any good for the renewable energy generation industry. As I spent more time understanding the industry, I realized it is a significant and untapped opportunity and concluded to get started in the field.
Renewable energy is energy that is collected from renewable resources, which are naturally replenished on a human timescale, such as sunlight, wind, rain, tides, waves, and geothermal heat. — wikipedia
Types of Renewable Energy
Since my area of interest in Solar Energy, this post will heavily focus on that.
The sun’s light/heat’s conversion into a different form of energy generally comes under solar energy. The traditional way is to set up ‘Photovoltaic’ (PV) Solar Panels, which generate electricity when the sun is up in the sky.
Here is an image of a solar power plant.
Generally, a solar power plant is spread across multiple acres of land, and they tend to be huge.
Let’s dig deep and get a basic understanding of any solar power system.
Working of a Solar Power System
The fundamental working can be explained in simple words as the sun rays fall on the PV solar panel, the panel generates electricity in the form of DC current, the DC to AC converter converts electricity from Direct Current to Alternating Current which is then inputted to another system, e.g., grid or home.
Here is a diagram of house mounted solar power system
The solar modules generally mounted on the house aggregate the power to an inverter. The inverter converts the power to AC and passes it on to a fusebox, which supplies the current to household appliances or to the grid, depending on the use case.
For a house mounted system or a low consumption (domestic) load, the monitoring and logging is not a priority, whereas, for an industrial-grade system such as a solar power plant, monitoring is very important.
SCADA stands for Supervisory control and data acquisition. It’s a system that taps into different points in the power plant (here on, we will mostly talk about industrial-grade systems) and displays on monitors and stores the data on a permanent storage device such as a hard disk.
Basically, SCADA is any system that records the state of the system and monitors it.
Solar power plants also use SCADA systems to monitor various parameters of the plant. Given this data, there is a perfect chance of performing data analysis and doing machine learning to optimize certain objectives.
Application of ML in Solar
Artificial Intelligence will impact a lot of industries. For systems that show determinism to some extent, such as solar power plants, Machine Learning will make a huge change in which the systems are set up and operated.
On the top of my head, I could think of a few things where the application of ML makes perfect sense, e.g., energy demand forecast, failure analysis, operation & maintenance, Scheduling … etc.
It is interesting to think about how the new age of electricity (A.I.) is going to meet the challenges of systems generating old-age electricity.
Working in the solar industry for a couple of weeks has made me realize how ML could impact solar and its huge impact. We would be automating almost 80 percent of the work. With proper systems in place, we would keep the systems up and running for a longer duration and perform preventive management, thus increasing reliability and efficiency.
A lot can be done using ML with power generation and consumption, which was my introduction to the solar industry. I will not talk much about ML here as I am still exploring the industry and understanding it. I will record my ideas here as I come across and build cool things.
Please get in touch if you have any ideas or want to discuss further.
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