In the world today, among some of the top tech skills in-demand, machine learning has managed to secure its place. In this article, you will get to understand the concept of machine learning in simple terms, its areas of application and also you would also discover how valuable the skill, machine learning, is.
What Is Machine Learning?
Machine learning is seen as a subfield of AI, Artificial Intelligence. It involves the computer algorithms actually teaching themselves ways to solve problems without any human intervention of any sort. Okay, now that sounds like these computers would someday take over this world, right? But no, that is far from possible. You see, these machines can only function based on data supplied to them.
Machine learning actually enables these machines to take the possible routes to solve problems based on a data set provided. This data set could be labelled, could include the input and outputs or may just be introduced for the learning algorithm to make some sense of the data and generate something meaningful as the output. Thus, the machines actually “learn”.
This saves programmers time that would have been spent writing weeks load of programs and the job gets done pretty fast too. Apart from figuring out the best routes and solutions towards attaining a specific goal, Machine learning is also employed in making predictions for future outcomes based on available data.
Current examples of machine learning application includes recommender systems, fraud detection, generating customer insights, etc.
Machine Learning Approaches
There are basically three, 3, approaches to machine learning:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
Imagine you were given a hammer, some nails and some wood, with an assignment to create a furniture of some sort, say a wooden chair. Now, we know the expected product is a wooden chair, so we just have to figure out a way to combine these materials to produce one.
Supervised learning involves providing the computer with input and dictating the required output. This leaves the learning algorithm with the task to figure out how to achieve the desired goal, the output. Supervised learning is applied in text categorization, face detection, etc.
Unsupervised Learning involves a set of data containing only inputs. Therefore, the learning algorithm is expected to make some sense of the provided data to generate an acceptable output (the right output).
Some examples of Unsupervised machine learning are customer segmentation, recommender systems etc.
Reinforcement learning is an approach to machine learning where the learning algorithm figures a way to achieve a certain unnamed goal in a specific environment in order to obtain some form of reward. Using this approach, the learning algorithm learns from consequences of its actions while trying gain a reward.
Reinforcement learning is applied in robotics, business strategy planning, etc
Applications of Machine Learning
Generally, Machine learning is currently being applied in various fields, from the Agricultural sector to the health sector and even in games. Other fields of Machine learning applications are:
- Computer networks
- Fraud detection
- Data quality determination
- Handwriting recognition
- Machine translation
- Search engines
- Self-driving cars
- Traffic prediction
- Speech recognition and the list goes on and one.
Click here to learn more about machine learning applications.
Challenges of Machine Learning
- Unavailability of data: This may be due to lack of data from a specific field or lack of access of data as most establishments would prefer to keep their data safe from third parties.
- Data bias based on new data sets that are different from the training data sets.
- Wrong tools and wrongly picked algorithms
- Bad choices etcetera.
Demand for Machine Learning
Machine learning is not just a skill for the present but also for the future. With the rate at which machine learning is intercepting various fields, demand for machine learning would be on the rise now and in the future. Currently, around 49% of companies worldwide are either introducing machine learning to their field or planning to include it soon in operations.
The average salary for a machine learning engineer is marked at approximately $150,000 and there are more than 50,000 jobs on LinkedIn alone.
Where can I get a free machine learning course?
Tecky Energy would recommend you check out Google’s free machine learning course.
Now you know the basics of Machine Learning.
Machine Learning In Renewable Energy Technology
Machine learning seems to be penetrating deeper into various disciplines and industries. Now it has found itself here, machine learning in renewable energy technology. How is machine learning applied here and what is the role it plays? Time to find out.
What Is Machine Learning?
The basics of machine learning has been explained earlier and you will find that machine learning can in applied in basically any field. The whole concept is on the ability of machines to learn from data sets provided to them. These machines may be given guidelines, inputs and expected outputs and are assigned with the task of reaching final results without any form of instruction. They may also be assigned to make meaning of a certain data set and use provided data to reach conclusions and outputs. Sometimes, these machines even learn from the consequences of wrong decisions taken and implemented.
Now, even the energy industry is benefiting from this technological advancement. With this concept, we can work with renewable energy sources to optimize energy generation, energy consumption, energy demand and supply and lots more using mostly predictions made on basis of previously studied (learned) data. This would result in more energy saved and less energy lost, it would help mitigate global warming and in future, help to put a stop to greenhouse gas emissions.
Role of Machine Learning in attaining the Net-Zero Goal
Machine learning when applied in the renewable energy sector can play a big role in reducing the world’s carbon footprint. How? It’s simple. To mitigate global warming, we have to put a stop on emissions completely and to do so, it means fossil fuels have to go. If per say fossil fuels are inexistent, how then can we cope with energy demand? We simply have to increase the efficiency of renewable energy sources.
Read about efficiency of energy sources in this article: Find Out Which Alternative Energy Source is Exhaustible
To help increase efficiency in renewable energy, machine learning can play the following roles;
Predicting Energy Production
With the knowledge of the consequences of fossil energy creeping deeper into homes, people are beginning to turn to renewable energy sources. With machine learning in renewable energy technology, predicting future energy production based on weather conditions and availability of the energy source, is possible. These predictions are always precise and reliable. For example, there are days when wind power or solar power alone can generate half a country’s worth of energy, while some days aren’t as favorable but with machine learning in renewable energy technology, days like this can be accounted for and energy demands can be met.
As a matter of fact, it is even possible with machine learning to predict the viability of an energy project in a particular environment before the project is implemented. Thereby saving time and money.
Machine learning can help residence or buildings determine days and times when there would be less supply from their source of renewable energy and then they can be prepared to draw power from the grid on such days. With machine learning in renewable energy technology, one can predict just how much the weather would influence energy production for up to 30 days ahead. Yes!
Predicting Energy Consumption
If machine learning can predict renewable energy generation, it can also predict energy consumption. Renewable energy sources faces barriers in energy generation due to natural causes. If energy consumption is monitored, the supply of energy can go round even when there’s less energy production. Let me explain.
Say For example, Mr. A consumes twice energy as Mr. B daily. Instead of just supplying both parties same quantity of energy per day, Machine learning in renewable energy technology would help recommend the quantity of energy to supply to both Mr. A and Mr. B that would satisfy both energy needs and requirements. If energy consumption changes with time, machine learning can also spot the trend and supply accordingly to residents, offices, industries etc.. No energy wasted, No energy lost. More savings.
Automating Energy Consuming Appliances
When we talk about Machine learning in renewable energy technology, it cuts across to home and office appliances too. Attaining the net zero goal with machine learning would require that we design new renewable energy products that can adjust their energy consumption with change in energy requirements. Electric appliances, like light bulbs could turn off automatically at a time of the day when everyone’s out or when residents usually turn them off. Another good example is thermostats. Thermostats powered with renewable energy could turn off and on at will and carry out its own operations at will.
Empowering these machines with machine learning would enable them design and decide their own operation based on what they learned on their own at home, in the office and anywhere. This ensures that we are not wasting energy and renewable energy gets to be saved for the future.
Also in areas where blackouts happen regularly and are likely to happen, automated machines with machine learning can detect and respond faster in terms of blackouts.
Predicting Potential Problems
Carrying out regular maintenance is fine, but if we had a hint on when exactly a machine needs maintenance, we could save the life of the equipment, save money, time and increase efficiency.
With machine learning in renewable energy technology predictions on when and where malfunctions or breakdowns can happen. If predictions on problems affecting the energy grid can be made before hand in the energy technologies, then steps can be taken to prevent these issues. We all know how expensive these techs are and we want to make the process cost effective, not the other way round. Also, with predictive analysis, machines can effectively determine when machines would require maintenance instead of carrying out scheduled maintenance operations that may not be necessary.
Merits of Using Machine learning in renewable energy technology
In a nutshell, the introduction of machine learning in renewable energy technology will lead to;
- Energy savings.
- Less Energy waste.
- Increase in renewable energy demand. With increase in efficiency in the renewable energy sector due to machine learning, obviously demand would increase as reliability increases in the renewables.
- Faster response to blackouts.
- Reduction of fossil fuel usage. When the use of renewable energy sources increase, of course there would be a reduction in demand for fossil energy sources and fuels. Leading to a reduction in carbon footprint worldwide and a step further towards mitigating climate change and greenhouse gas emissions.
- Attaining the net zero goal and in general, increasing sustainability.
How can machine learning help to increase sustainability
Machine learning in renewable energy technology can most definitely help boost sustainability as explained above. The more efficient renewable energy technologies, supply and efficiency gets, the less fossil energy consumed. The less unclean energy consumed, the higher sustainability and the greener the world becomes.
Although unclean energy may be here for a while we still have a chance to build up the renewable energy sector with machine learning. Let us make our environment cleaner today.