How can AI support solar energy production?
In recent years, skyrocketing data availability and computing power have enabled machine learning algorithms to offer better predictions about the fluctuations mentioned above.
Machine learning is a fascinating branch of AI. It specializes in creating computer algorithms that can automatically improve their performance through experience.
ML algorithms are able to recognize specific patterns in data sets, build mathematical models to represent them, and use these models to make predictions or decisions without being explicitly programmed.
A further step forward has been taken with the rise of deep learning, an evolution of machine learning that mimics the mechanisms of the human brain to process information by using deep artificial neural networks (ANNs).
All these innovations have been implemented in the field of solar energy in a variety of ways, including:
- AI-based forecasting
- Smart power grids and storage units
- Drone technology for solar panel inspection
- Market expansion
1. AI-based forecasting
Nowadays, machine learning models are widely used to improve forecasting activities and stabilize solar energy production. Among them, we can find SVM (support vector machines), gradient boosting decision trees, and many others.
Researchers are testing various models under different conditions and locations to identify the most efficient ones, as their performance is still widely debated.
In addition to ML models, forecasting can also count on more traditional statistical methods (especially time series models), which are mathematical formulas and techniques used historically for this kind of purpose.
Hybrid models are better
Actually, the experimental results showed that hybrid models combining ML methods and statistical methods achieved higher accuracy than pure ML models.
The superior performances of this mixed approach have been tested in various studies.
Among the many, Gigoni’s on the K-NN and quantile random forest machine learning algorithms, or Feng’s research about two-layer ML hybrid models to predict one hour ahead solar irradiance
That is why both machine learning and traditional time series techniques are commonly used for forecasting.
“The experimental results showed that hybrid models combining ML methods and statistical methods achieved higher accuracy than pure ML models.”
Forecasting models should be trained properly
As you may have guessed, there is no perfect model for forecasting. The accuracy of each model may vary based on the climatic conditions of the intended location.
This is because every machine learning-based model must be trained and accumulate experience to function properly.
This means that a model will work better if it has been trained with data collected on a specific site and during the time of year for which predictions need to be made.
Overall, adapting a specific model to a given environmental dataset can increase predictive performance over using a generic model trained with a representative dataset of many weather regimes.
That’s right, machine learning models have their comfort zone too!
2. AI to boost power grids and storage units
AI is not just the solar energy industry’s trusted clairvoyant. Algorithms are also reliable tools for improving power grids and for dealing with storage problems.
In fact, power grids can be equipped with numerous sensors to collect a large amount of data.
When analyzed by artificial intelligence, this data provides valuable information to network operators to provide greater control and flexibility.
For example, we can combine energy flow control systems with large industrial equipment, such as air conditioning units and furnaces, to automatically shut them down when power is low.
Making storage units smarter with AI
Energy storage technology plays a fundamental role in a context such as that of renewable energy, which is subject to seasonal dynamics.
To function properly, battery hardware must be associated with sophisticated software that controls power generation and consumption.
Basically, we are talking about intelligent storage units powered by AI and adjustable according to the supply flow.
Artificial intelligence and especially machine learning, by creating models based on previously collected data, can help manage these flows and store excess energy to avoid grid overloads.
Do you remember “Monty Python’s The Meaning of Life”, specifically the fat guy exploding?
That’s what we want to avoid.