When Energy Meets The Internet of Things technology: IoT in Renewable Energy ? Why? How?! What for?! With the awareness about global warming and depleting of fossil fuels being on the rise, the world is shifting towards alternative sources of fuel to power its needs. Of them, the most sustainable option being the renewable energy. The field is massive spreading across various areas of energy resources such as solar, the wind, hydro and geothermal. The market is on a global scale and it is fast becoming one of the most thriving businesses on the planet. With strong market, comes stiff competition. Hence, efficiency, operating costs, profitability and productivity has taken a prime importance. The nature of which cannot be just improved by mechanical or electrical engineering but with the ability to continuously monitor and maintain high performance over the time. This is where IoT kicks in.
Why IoT when there are a plethora of technologies already?
The answer lies within the fact that not everyone can be as equipped to create, sustain and maintain a conventional energy industry. These industries typically go upward to a six-figure dollar value in monetary worth to just lay a basement or a foundation for the said industry. But this is the one good thing about having an alternative means to energy- They come in all shapes and sizes. This means every hundred dollars out there could fetch you your own electricity generator.
Imagine the world where you can not only make and use your own electricity, large enough to accommodate your wild cravings, multiple air conditioners, centralized heating, hot water showers, you name it. The real awe is when you can use this all and still export power to the central grid to earn money! This is what renewable energy offers. The process, however, isn’t that easy to handle. Be it a hydro-power plant small enough to run on the stream or creek that flows near your home or be it a small wind power turbine generator you had installed near in the field near your rural residence or a vast solar farm or even a small solar cell array atop your home in a typical urban setting, it all requires numerous variables that need be monitored and maintained that will result in the efficient operation of the said power source. This process will prove to be insanely difficult for manual labor to perform efficiently. It is impossible to have dedicated PCs to monitor for every individual component of the system. This is where IoT ‘s potential can be unleashed. Be it the blade resistance to the wind for a wind turbine or the cell’s individual temperatures in a solar farm, every imaginable component can be monitored, sensed, data collected and also be uploaded to the cloud for processing. This process is exponentially efficient as when compared to a single dedicated PC handling all the variables.
Okay, this is all good for the common man. I own a renewable energy industry. How will this help me?
Well, many drops make the mighty ocean, right? The sensors and devices used in IoT and its immaculate operations may be small, but make no mistake, it is the multi-billion dollar industry in the wake and its immeasurable potential will touch every other sector there is, most prominently in the energy sector.
If you are an industrialist looking for IoT’s applications on the renewable energy side, it is a fact that you will very well be knowing about Predictive maintenance. If conventional predictive maintenance can be so much powerful as a tool even when done my manual sensing and predictive calculations (which is done with a specific time interval and prone to errors), then imagine a set of sensors working like bees round the clock, measuring all parameters every second, and performing predictive calculations provides a means of predictive maintenance like no technology has ever offered.
From a technical point of view, renewable energy sector will be improved in the following four major ways:
- Big data analytics, faster analysis of sensor outputs.
- Breaking up of analytical data for the non-tech savvy.
- Decentralization of data.
- Self-learning machines, Artificial Intelligence machine learning.
Big data analytics, faster analysis of sensor outputs:
Simply put, when you have so many data to crunch in so less time, so small enough that you cannot possibly arrive at a conclusion in time for an action, big data is the key. Typically from the above passages, it will be quite clear that in renewable energy sector, for an efficient operation, a humongous amount of data is being transferred and analyzed to get optimized outputs. With the big data analysis and the cloud storage capabilities, companies and startups in the energy side of things will get to faster results and ultimately, better efficiency.
Breaking up of analytical data for the non-tech savvy:
If you were to be a non-engineer and still own or manage a renewable energy farm, things could get wildly complicated for you. With all the kilowatts per hour data, actual energy-apparent energy mumbo-jumbo, you could easily get lost in the wilderness. Hence, all the data that is being processed should have to be converted to user understandable data, like, graphs, pictograms, pie charts etc.
Decentralization of data:
The downside of using big data for analysis is storage capabilities, be it the cloud or the old school hard disk storage on site, is very limited as when compared to the data that it collects and stores. Large data storage means large space occupation, larger security risk and subsequently the costs that accompany this. Hence, a decision must be made so as to decide what data to keep and what to delete. All the essential data should be kept for the future calculations and usage trends. The conventional way to store the said data would be on a centralized server, but that is in the past. With the advancement in the decentralization of database techniques, all essential data could be stored in individual data holding centers.
Self-learning machines, Artificial Intelligence machine learning:
If you had seen the movie called ‘Person of Interest’, you know how important this machine learning and artificial intelligence is
gonna get in the coming days. In the terms of the energy sector, this will be used in the prediction of the future load and computing the real-time big data and storage of the said data. As from the previous heading, the decision to be made so as to keep what data and delete the rest cannot be humanly done within an acceptable time limit. Hence leaving the mundane tasks to the AI. In due course of time, all the computing decisions could be made entirely by the AI for most efficient operation of said sectors. Here’s hoping Terminator doesn’t become a reality, though.