Category: Computer Science

How We Can Leverage Smart Building Data to Combat Climate Change

How We Can Leverage Smart Building Data to Combat Climate Change

How We Can Leverage Smart Building Data to Combat Climate Change

11/08/18

“How can we use data from buildings to make them better for the environment?”

 

The ascent of the Internet of Things is changing everything in its path, and the building sector is no different. With our newfound access to all sorts of building data, we can take much more control to ensure energy efficiency. Whether this means finding bottlenecks in the system, more optimal times to control HVAC systems or better building automation systems.

 

Image credit http://www.ledesigngroup.com

How IoT can help Wind Turbine Maintenance

How IoT can help Wind Turbine Maintenance

How IoT can help Wind Turbine Maintenance

09/21/18

“How can IoT help make sure that wind turbines are operational?”

 

The Internet of Things is on the rise. From building automation systems to fleet platooning, it can be found everywhere. But did you know that IoT can help Wind Turbine Maintenance? Well, it turns out that by placing sensors which can monitor the performance of internal components, we can predict when systems are going faulty. This will have monumentous implications with the ever-increasing supply of wind energy.

 

How Accurate Weather Prediction can Help Grid Resiliency

How Accurate Weather Prediction can Help Grid Resiliency

How Accurate Weather Prediction can Help Grid Resiliency

09/06/18

“How can weather forecasts help stabilize the grid?”

 

Weather forecasts have become apart of our daily lives. But did you know that they can also help with grid resiliency? By being able to forecast the future, we can obtain how large our cooling loads will have to be. And with this knowledge, we can get ancillary reserves ready when the time comes. This way, Accurate Weather Prediction can Help Grid Resiliency!

 

Loss Functions

Loss Functions

Loss Functions

07/03/18

“How can we see the cost of some data?”

 

When we are working with data, we often want to pick the best value. However, it can often be hard to quantify which value has the highest related cost. So how can we use our scientific problem? Well, we can use something called a Loss Function which maps all variables to a particular cost. 

Fitness Functions

Fitness Functions

Fitness Functions

07/02/18

“How can we evaluate a solution as a single figure of merit?”

 

In many fields of science, one of the most important things to do is to evaluate which solution function to a given dataset is the best. However, quantifying this can be very difficult. So how can we use our scientific mindset to solve this problem? Well, we can simply use something called a Fitness Function that can summarize all functions as a single figure of merit. With an easily accessible and quantified set of possible functions, we can pick the best one from the stack. Fitness functions are commonly used in Data Science where there can be a manifold of possible solutions.

LIDAR

LIDAR

LIDAR Scanning

06/23/18

“What makes autonomous vehicles work?”

 

Autonomous vehicles, archeological scanning, soil imaging, and automated agriculture all rely on knowing real-time distance to work. However, how exactly can we accomplish this? Well, let’s use our engineering mindset to find out. We know that if we send a beam of light from a laser somewhere, then it can be reflected back. And since the speed of light is constant in a uniform medium, if we measure the time it took to return, we can obtain a measurement for distance. This system is known as LIDAR Scanning,  which stands for Light Detection and Ranging. LIDAR has caused a revolution in multiple fields of science and is moving every day closer and closer to mainstream adoption.

Feature Selection

Feature Selection

Feature Selection

05/29/18

“How can we select relevant features from data?”

 

Engineers and Scientists (both Natural and Social) analyze data to see what causes what. Often times, they will start with a set of factors that they think that is relevant, and trim down what does not have any effect. This is known as Feature Selection and is one of the underlying tools behind statistics and machine learning.