Machine Learning and IoT for Energy Management

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Machine Learning and IoT for Energy Management

Machine Learning is when algorithms parse massive sets of data to the computers so that they can act without being Explicitly Programmed. It mainly focuses on the development of various computer programs that can change when exposed to new data. IoT is Interconnection of various smart devices for better communication. Before we continue, in the previous blog I have explained in detail why both the technologies are dependent on each other, please have a look at that. In this article, I will cover some of the brief points on how to use machine learning and IoT for energy management.

Where can machine learning be used?

Machine learning can be used in Probabilistic Energy Forecasting. For the people who don’t know, what’s probabilistic forecasting? Probabilistic Forecasting mainly sums up all the points that are known or any estimates regarding the future events. In short words, Probabilistic Foresting is meant for making various assumptions about the future events. Some of the key areas where probabilistic forecasting can be used are as follows:

Electric Load Forecasting: The main purpose behind this is to come up with the probability distribution of hourly loads simultaneously.

Electricity Load Forecasting: The main reason behind this is to estimate the probability distribution of electricity price in various zones simultaneously.

Wind Power Forecasting: The main purpose behind this is to estimate the probability distribution of wind power generation from more than one wind farms simultaneously.

Solar Power Forecasting: The main purpose behind this is to estimate the probability distribution of solar power generation from more than one solar farms simultaneously.

Besides this, there are some machine learning algorithms which can be used for Energy Forecasting as follows:

  • Clustering Algorithms
  • Artificial Neural Networks
  • Regression Models

Besides Energy Forecasting, Machine Learning can be used in Buildings. For example, Machine Learning can be used in Hospital, how space can be used to integrate various smart IoT devices for best consumption of data with low cost.

How Deep Learning and AI can be used in Buildings?

Dear Learning is such a kind of machine learning, which uses Neural Processing to become the best at a particular task in a given time. Deep Learning is used to increase the capacity of IoT by finding patterns used in building space.

Artificial Intelligence can be used in two ways, weak and strong. In weak AI, the computer will only focus on a single task while in strong AI, the computer will think, find the reason, learn the situation and then act in a creative manner by considering the environment.

Strong AI Buildings will then be used for deeper communications in building AI, which will require speech recognition, touch, movement and observing human activities from various parts of a building.

Why should IoT be more focused in Industrial Development?

Various Large Scale Industries have a large amount of data collected from various wind turbines, jet engines, and MRI machines, this data holds more potential business value than data collected from the social web using Big Data. By introducing Big Data and IoT in various Industrial Companies, all the operations and activities can be performed in much faster way. Besides this, data can be easily organized in the cloud and can also be analyzed in the case of any problems.

How to Integrate IoT for optimum utilization in case of Energy?

  1. Make Energy Production meet the Energy Consumption:

With the current energy ecosystem, analytics is maintained from both consumer and Industry side. Also, various automated systems are being introduced, but interaction is reduced. So, Efforts should be made to save Energy Consumption.

2. Create Incentives for the Consumer:

Consumers should make effort to control their usage of Energy. For example, if a user wants to use Dishwasher at night, but doesn’t care about the use of it. Instead, the user should give the control to the utility, so that utility will complete the task in a particular time. By this, Utility should provide user to save a point of electricity. This system can also be implemented in Electric Cars and various other devices.

3. Large Scale Automation of IoT Energy Devices with utility companies

The main goal behind this is to have the prior optimization by automatic communication between the Energy Sources and Energy Loads.

By implementing all the above methods of IoT, Machine Learning, and Artificial Intelligence, we will be able to manage the Energy Resouces in the most optimized manner.

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