green-energy-production-inverter-prediction
The project aims to use AI to improve the prediction of weather and equipment failure in order to optimize power generation from solar panels.
The project also includes monitoring and analyzing power usage to optimize the entire power grid. The AI will also predict the necessary capacity for new systems for customers and provide personalized recommendations for solar panel installations based on individual customer's electricity usage patterns.
The cost of this project is estimated to be around 200,000 dollars.
Problem Scoping
The project aims to use AI to improve the prediction of weather and equipment failure in order to optimize power generation from solar panels. The project also includes monitoring and analyzing power usage to optimize the entire power grid. The AI will also predict the necessary capacity for new systems for customers and provide personalized recommendations for solar panel installations based on individual customer's electricity usage patterns.
Dataset: Solar Power Generation Data
Questions
- Can we predict power generation over the next day?
- Can we predict the yield of solar power generation system and use that to predict potential future failure of the system?
- Can we identify underperforming systems?
- How best can we predict power supply over the next month?
- Can we predict the typical or max instantaneous power generation versus ' consumption to explore the best battery size to store the excess.
Reasoning for doing such a project
Performance, time & cost
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Predicting weather in a better way using AI and therefore knowing how much power we will be able to create
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Predicting when machinery and equipment will fail and therefore make maintanance calls in advance to reduce to down time
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Be able to understand what power is solar coming in, at what times of the day and in the year so that whole grid as a system can work efficiently
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This Machine Learning model will be able to also predict how much capicity we need for new systems for customers
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Be able to know what to sell to small customers who want to have a solar panel setup based on the features of electricity usage, how big is the house, number of people in the house, ages of adults, hobbies they have, etc, to define how many solar panels they need
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The average ML company will most likely charge 200,000 to be able to create this engineering project,understand offsets, surplus and capacity and create a possible grid-balance in the future
Value Areas
Let's determine the top five value Areas (Prevent, Project, Produce, Promote, Personalize) that could make this project a business scenario for other use-cases
- Prevent - Transmission loss, inverter failure
This area focuses on preventing transmission loss and inverter failure in order to ensure that the power generated is delivered efficiently and effectively to the end user. This could include measures such as regular maintenance and monitoring of equipment, as well as implementing fail-safes and redundancy systems.
- Project - Power generation for next few days
This area focuses on forecasting and estimating power generation for the next few days, in order to make sure that the necessary resources are in place to meet demand. This could include using data and analytics to predict changes in weather and other factors that may affect power generation, as well as developing plans to respond to unexpected events.
- Produce - Production of green energy
This area focuses on the actual production of power from solar panels, including the design and installation of the panels, as well as the optimization and maintenance of the systems to ensure maximum power generation. This could include researching new technologies to improve panel efficiency, and monitoring the performance of the panels to identify and troubleshoot any issues that may arise.
- Promote - Clean energy
This area focuses on promoting the use of clean energy, such as solar power, to reduce dependence on fossil fuels and reduce the environmental impact of power generation. This could include education and outreach campaigns, as well as incentives for individuals and businesses to switch to clean energy sources.
- Personalize - Customize recommendations for solar panel use
This area focuses on providing customized recommendations for solar panel use to individual users. This could include using data and analytics to understand the unique needs and usage patterns of each user, and providing tailored advice on how to optimize their solar panel usage in order to save energy costs and reduce their environmental impact.
Discovery
- Discover how much surplus we have (e.g. what can be used in other applications like farm)
- How much can solar offset regular power consumption (non-clean energy)
Hypothesis
A judgement analysis of when my machine learning model produces a right and a wrong answer
If there will be surplus, and there probably wouldn't be based on consumption information we have seen.
A wrong answer could lead to a burn out if other electricty producers reduce their output based on the solar plants predicted generation value.
Also a wrong answer would not be able to offset as much of the non-renewable energy and not meet the predicted goals of meeting the green house gas emissions targets.
If the predictions are wrong, it would underperform the plant and reduce the confidence in the model.
If the predictions are wrong, it could reduce the confidence people have in renewable energy.
If the model is right, we can determine how many solar panels to add to the system and reach out energy goals.
If the model is right we can better prepare the state/country for disasters.
Defining the question, and aligning with business outcome
What is the power generation for "a" day?
The answer we're looking for
Based on the predicted power generation, do we have surplus power.
Problem Context
Power consumption data will be important for us to determine if we have surplus power available.
One of the things we are missing is how much power is being used, if there are any loads, since this will determine how much power we have for a more in-depth analysis on the amount of power we have.
Ethical Considerations
Are there any ‘red flags’ or issues of bias that arise for employees, users or from the dataset being used?
Ethical considerations must be taken seriously. For example, mining that is related with solar panels is an issue, with rare earth metals making up the components of the panels.
Political discussion around clean energy and the mining of rare earth metals to produce the components for clean energy.