Using Computer Science to Improve Wind Power

        Imagine a future where billions shiver in darkness due to the scarcity and expense of fossil fuels. This dark dystopia could become reality without renewable energy. 

Problem: We are embroiled in an energy crisis, and the clock is ticking for us to find a renewable alternative to fossil fuels. Fossil fuels are finite, and the combustion of fossil fuels creates pollution. Air pollution kills 7 million people per year -- 1 in 8 of total global deaths, according to the WHO. 

               While wind power is a feasible alternative, inaccurate predictions of turbine power output hinder the growth of wind power. Inaccurate predictions force utility companies to schedule backup power plants to account for wind intermittency. This can waste energy. Inaccurate predictions can cause utility companies to waste money because backup plants are expensive to maintain.

My Innovation: To address this problem, I used machine learning to create HAWC: Hybrid Algorithms for Wind-power Computation. Machine learning is a type of artificial intelligence that allows computers to learn from existing datasets and predict outcomes for new datasets.

 

 
How HAWC Works: I divided my project into three steps: (1) testing the most basic machine learning algorithms; (2) coding the algorithms most widely used in the field, and (3) improving the existing algorithms. Each algorithm I tested predicts wind turbine power output given a dataset of wind speed from the NREL.

First, I coded linear regression and polynomial regression. Linear regression uses a linear function to make a prediction, generating a straight line that fits the data. Polynomial regression, similar to linear regression, generates a function that fits the data. However, it uses a polynomial function to create a curved line. This can be more effective on unpredictable data sets such as those of wind speed.

While these algorithms are accurate, they may have lower percent accuracy on extreme data values. Therefore, most scientists use 2-layer neural networks to predict turbine power output. Neural network algorithms imitate the decision-making process of the brain by using an iterative predicting procedure. Similar to neurons in the brain, neural network algorithms are organized into interconnected ‘layers’ that function together to make a prediction. A neural network often consists of an input layer, at least one hidden layer, and an output layer, where data enters through the input layer, is processed in the hidden layers, and is expressed in the output layer.

Although the 2-layer neural network is one of the most accurate algorithms, I thought I could do better. I coded a 3-layer neural network which has not previously been applied to the field.The 3-layer neural network was the most accurate with 97.65 percent accuracy! 

How HAWC Can Help Solve The Energy Crisis: 

      My innovation will help reduce the need for wasteful backup plants, saving utility companies energy and money. By reducing waste, my project can give utility companies a stronger incentive to use wind power. This can enable the growth of wind power. By enabling the growth of wind power, my innovation can decrease the demand for fossil fuels, reduce pollution, and save lives.