UNDERSTANDING ENERGY CONSUMPTION USING MACHINE LEARNING, AUTO AI

In this write-up, I will be explaining how the technology made available by AutoAI has been helping to bring about sustainability and reduction in energy consumption. From my various researches, machine learning and energy consumption are topics that are recently gaining attention in recent times.

It is no gainsaying that the rate at which humans use energy has been growing astronomically and this in turn generates a greenhouse gas emission. In the same vein, there is a huge development in machine learning, and more advanced algorithms from the platform of IBM have proved that the application of machine learning to the energy sector will make its future turn out brightly.

My introduction of machine learning and AutoAI into the energy sector has turned out to be a prospective one, with a great forecast of energy consumption and performances.

LET’S TALK OF WHAT MACHINE LEARNING IS

Okay, I will reduce my noise to the barest minimum in order to ensure that those who are hearing about machine learning for the first time are on the same page with me.

Machine learning is the process of making a software program learn the best way of increasing its effectiveness using a model via an algorithm on a given task while taking into consideration garnered experiences by itself.

Guess I just succeeded in confusing you more. Okay, a model is more like a vending machine which when provided with an input (money) will, in turn, provide you with an output (probably a soda can) while an algorithm is a model trainer, it helps with the decisions a model is expected to take on a giving input to give expected output. Hope that helps?

Its developed statistical models and algorithms are expected to have gone through a period of research and improvement using processed data from which they learn from.

NATURE OF REQUIRED DATA

Well, any relevant voluminous energy historic data would suffice when it comes to machine learning, and the more voluminous the better. Trained data are mostly used in the energy industry and this ensures accurate prediction of the estimated future consumption of energy via loads or devices.

HOW MACHINE LEARNING AND AUTOAI SUCCEEDS IN FORECASTING ENERGY CONSUMPTION

With respect to energy consumption, most times we are caught in the web of maintaining our habits until an unexpected phenomenon triggers our consciousness to the huge electricity bill staring at our face or in most cases a lower than anticipated performance from our renewable source of energy.

This is the exact point where machine learning and AutoAI come into play as they help in predicting energy consumption using some analytical methods.

By processing the historical data derived from a period of energy consumption, it becomes possible and easy for the generated model to show past trends while also predicting the future patterns of energy consumption.

BENEFITS DERIVED FOR ENERGY CONSUMPTION PREDICTION

1. Economic Benefit: This makes it easy to translate your consumed energy into cost that helps you in making an informed decision on your energy bills

2. Technical Benefit: A well-managed energy data opens a broader and better perspective into what it takes to collect and analyze energy data which would foster better and more accurate predictions.

3. Practical Benefit: Habits that put you under a skyrocketing bill situation are more consciously controlled without having an adverse effect on your life quality and productivity by knowing the rate of energy you would consume, in what manner, and on what purpose.

POSSIBILITY OF PREDICTING RENEWABLE ENERGY CONSUMPTION

While I considered the economic and practical aspect of predicting energy consumption, the good impact of the prediction on consumers as well as the reduction in the expected environmental impact of the production and consumption of energy cannot also be overemphasized. This is another great machine learning engendered benefit.

We need to understand that there are associated significant economic and operational cost associated with the inability of electricity network providers to predict the expected energy generation from say wind turbine or solar panel as this could also cause a great deal of destabilization to the power grid.

Therefore, the ability to collect weather and other climatic condition data and feed them into AutoAI, a machine learning software has proved beyond reasonable doubt that it is possible to forecast the amount of energy that would be generated in a given time, if it would be enough or not in order to quickly transit to non-renewable energy form if necessary within ample time