Using Machine Learning for Solar Heat Prediction

Using solar energy for heating and hot water is a smart idea because it saves money and helps the environment. However, to make sure there is enough heat for homes all year round, we often need backup systems like boilers or heat pumps. The key to managing these systems well is being able to predict how much heat solar energy will produce.

To do this, researchers use experiments and computer models to create a performance curve for solar collectors. This curve shows how much heat the collector can produce based on the amount of sunlight and other outside conditions. It’s important to note that this performance curve can change over time as the solar collector is exposed to the weather. Therefore, to control these systems effectively, we need a way to predict Heat Production accurately and adjust as needed.

This article discusses a new approach that uses Machine Learning to predict solar heat production. This method adapts to changes automatically, making it useful for small home installations. We aim to create a system that relies on low-cost sensors and data from public weather forecasts.

#Solar Energy Efficiency

Using solar collectors, like solar thermal or photovoltaic-thermal (PVT) systems, can meet heating and hot water needs effectively. These systems offer benefits such as lower operational costs and a smaller environmental impact. In sunny seasons, solar energy becomes sufficient enough that traditional boilers aren’t needed. However, to meet demand in all seasons, we usually add auxiliary heating systems like boilers or heat pumps.

The controller for these heating systems should minimize the use of backup heating. This can be done by storing hot water in insulated tanks, allowing the system to use excess solar energy when it is available instead of relying on backup systems. While managing space heating tends to be straightforward, combining it with domestic hot water heating can be more difficult due to spikes in demand for hot water.

To predict how much heat solar collectors will generate, we can use two main approaches: reliable user patterns and accurate solar production forecasts. Knowing how much heat a collector can produce is crucial to ensure that there is enough hot water when people need it.

Different methods have been developed to estimate the performance of solar collectors, including regulations and standards. These standard methods help predict long-term performance by providing important parameters based on controlled testing.

Numerical models have also been created to estimate heat production based on various factors like heat transfer mechanisms. However, these models have a major drawback: the performance curve of a solar collector changes over time as it is exposed to real-world conditions. To address this, we need a method that can adapt and improve predictions based on actual outside conditions rather than relying solely on initial performance data.

#Machine Learning for Solar Prediction

Machine learning offers a good solution for predicting solar heat production. It can create models that learn and adapt over time using data collected from sensors. Several studies have shown that machine learning techniques can create effective predictions for solar thermal collectors.

Most of these models use solar radiation and ambient temperature as input data, as these factors are critical for predicting performance. However, much of the existing research focuses on designing collectors rather than controlling heating systems.

Recent studies have used various machine learning techniques to assess how different factors, like fluid flow rates or working fluids, impact collector performance. These studies suggest that machine learning can provide a cost-effective and faster alternative to traditional testing methods.

In our approach, we focus on adapting models for small home installations. We want to create a system that can learn from low-cost sensor data and public weather forecasts. By integrating machine learning and taking into account the unique characteristics of solar collectors, we can develop a robust prediction system.

#Time Representation in Machine Learning

One challenge in working with time-series data is that it is often not collected uniformly. This means that data can have gaps, different collection rates, or misalignment among variables. To handle this, researchers have come up with methods to reconstruct the missing data or to work directly with what is available.

One common way to address irregularly sampled data is using techniques like interpolation to create a full dataset. Other methods adjust existing machine learning models to make them suitable for irregular time series.

Another innovative method uses Transformers. Instead of processing inputs one after another, Transformers analyze all inputs at once. This allows for efficient processing but lacks a natural way to represent time. To improve this, researchers suggest adding extra time information as input features.

For example, using sine and cosine functions to represent time can help the model understand periodic patterns better. However, this method relies on knowing the periodic patterns in advance.

To enhance how these models represent time, the Multi-Time Attention Network (mTAN) was developed. This model learns time patterns and trends automatically, making it more flexible and effective for predicting solar heat production.

#Advanced Control Strategies for PVT Systems

Our focus is on developing advanced control strategies for small home installations using PVT systems. In these systems, hot water can be produced from solar energy and used for both space heating and domestic hot water.

The idea is to use solar collectors to generate hot water efficiently, but backup systems are also used to ensure demand is met during less sunny periods. The system has to decide how to allocate available hot water between space heating and domestic hot water needs while minimizing the use of backup heating.

Our experimental setup includes various sensors that monitor the system's performance. We have tested the setup for both office and domestic heating needs. The primary factor in controlling the system is comparing expected heat production to domestic hot water demand.

We can calculate heat production based on sensor data that measures water temperatures and flow rates. It is also possible to use the efficiency curve of the collector to predict heat production based on weather data.

By continuously updating the machine learning model with real sensor data, we can keep the predicted efficiency curve relevant. This approach allows for more accurate predictions of heat production, accommodating daily variations in solar energy.

#Challenges and Solutions in Data Collection

When collecting data from sensors, it is common to encounter issues such as missing values or errors due to sensor malfunctions. This can affect the accuracy of our predictions if not handled properly.

To overcome these challenges, we use various interpolation methods, which estimate missing data based on available measurements. It is crucial to ensure that the dataset used for training machine learning models is accurate and complete as much as possible.

Additionally, we focus on simplifying the prediction task. Instead of predicting exact values, we can group predictions into categories (bins) based on thresholds. This helps reduce uncertainty and makes predictions more manageable.

By using historical data on weather, along with our machine learning approach, we can gather insights into expected solar heat production. This information can improve efficiency and responsiveness to user demand.

#Experimental Setup

Our experiments take place at a pilot building that uses PVT systems to generate heat and hot water. The building has sensors installed to monitor solar radiation, ambient temperature, and water temperatures.

We collect data over a full year to understand the system's performance under different conditions. Our goal is to create a reliable model for predicting heat production based on the collected data and weather conditions.

In this initial phase, we choose to work with actual weather data instead of forecasts. This helps us eliminate uncertainties related to predicting weather conditions, allowing us to focus on how well the model can predict heat production based solely on available data.

#Machine Learning Model Development

To develop our machine learning models, we compare three different approaches: a conventional Recurrent Neural Network (RNN), the CycTime model with fixed time embeddings, and the mTAN approach that learns time information.

Our experiments involve training these models on the collected data, focusing on predicting whether solar production will meet heating needs. We aim to achieve high accuracy while ensuring that the model can adapt to changes over time.

During the training process, we track how well each model performs and make adjustments as necessary. The goal is to find the most effective approach for our specific application.

#Results and Observations

After evaluating the performance of each model, we analyze the results to determine how well they predict heat production. The models are assessed based on their accuracy and their behavior when errors occur.

Overall, we find that attention-based models, like mTAN and CycTime, perform better than the traditional RNN approach. However, the tactical behavior of these models when making predictions, especially when the predictions are wrong, is also important.

The mTAN model shows a good balance between accuracy and behavior during uncertainties, making it a suitable choice for controlling the heating system. This model allows the controller to make informed decisions based on different scenarios, improving overall efficiency.

#Errors and Challenges

Some of the most significant prediction errors occur during the morning hours when conditions can be unpredictable. The dual nature of early mornings means that sunny days can quickly shift to darker, cooler conditions, adding to the challenges of making accurate predictions.

By analyzing the types of errors that occur, we can improve the models further to accommodate such fluctuations. We aim to enhance the models’ ability to handle periods when the sun is not shining brightly, ensuring there is adequate hot water available.

#Future Work and Conclusion

Our research focuses on advancing machine learning techniques to enhance solar energy systems' efficiency. The insights gained from this project can help in developing smarter control strategies that can adapt to changing conditions and forecasts.

Going forward, we plan to test our models using weather forecasts to understand how uncertainty in weather predictions affects heat production. Exploring ways to control the system dynamically will help balance uncertainties in demand, weather, and system performance.

By continuing to refine our approaches and integrate new findings, we aim to optimize renewable energy usage in domestic settings more effectively. This work is supported by funding from European Union and Greek national funds, emphasizing the importance of advancing renewable energy technologies for sustainable use.