Pre-trained Models in Energy Management: Challenges and Insights

Data-driven models are becoming very popular for studying complex systems, especially in Energy Management. These models often need a lot of data to work well, but gathering this data can be tough due to limitations in sensors and privacy issues. This is especially true in areas like energy management. In recent years, a technique using Pre-trained Models has gained attention. These models use data from similar contexts, allowing for better predictions without needing as much actual observational data.

#The Problem with Limited Data

In fields outside of computer science, like energy management, getting enough data can be a challenge. This is often the case when predicting demand for new products or services. In many situations, there just isn't enough data available, which is known as the cold-start problem. Observational data can also be limited due to the high costs of sensors and concerns about people's privacy. In the energy sector, for instance, data on energy usage is often collected over time, making it necessary to model and predict this data. However, setting up and maintaining the equipment to track energy use accurately can be both expensive and intrusive.

#Introducing Pre-trained Models

To tackle these issues, researchers are looking at ways to lessen the data needed for learning algorithms. One of these ways is the use of pre-trained models. These models rely on either simulated data or historical data, reducing the need for fresh observational data. Recently, there has been significant interest in how these pre-trained models can be applied to distributed energy resources.

The goal of using pre-trained models is to see how they can speed up the process of making data-driven models for distributed energy systems, like hot water storage. Specifically, we aim to answer several questions. First, do these pre-trained models perform better than those based purely on data? Second, how do the size of the datasets used for training and the model adjustments affect the performance of these pre-trained models? Lastly, do the improvements from pre-trained models benefit all systems equally, and how does this impact their usefulness?

#Data Collection and Approach

To address these questions, we collected detailed data from multiple hot water storage systems over a year. The aim was to create a model that could accurately predict the state of the hot water storage system, which could then be used for further control actions like optimizing energy use and enhancing comfort for users.

For the study, we split the data into two parts. Eight of the systems were used to create a source set, which means that data from these systems was available before the experiment started. The remaining 16 systems made up the target set, which were used to build Local Models. A local model only uses data from one specific system during its creation. Even after a year of data collection, the amount of information from a single system is still quite small compared to what is needed for strong model performance.

#Model Architecture

The problem is framed as a simple prediction task: estimating the temperature of the hot water in the storage system. The model uses three key pieces of information: the time since the last heating cycle, the hot water demand since that cycle, and the initial temperature of the water. These data points come from time series that track user demand, the operation of the heat pump (whether it's working or not), and the temperature of the water.

To make these predictions, we selected a neural network with hidden layers. This type of model is common in research because it allows for capturing complex, non-linear relationships between inputs and outputs, which are crucial for accurately predicting temperature changes.

#Local Models vs. Pre-trained Models

Local models are trained based only on the data collected from individual systems. However, these local models often struggle to perform well when they have limited data. On the other hand, pre-trained models leverage data from multiple sources. They go through a two-step process: first, they train on a larger dataset from similar systems, and then they fine-tune with localized data.

Using the same model architecture, we compared the outcomes of local models and various pre-trained models. We explored how the amount of training data, the process of fine-tuning, and the overall size of the datasets affected the results.

We created different models that used either a large dataset (data from eight systems) or a smaller one (data from only one system). After training, we evaluated how these models performed in real homes using the same local datasets for testing.

#Results: Performance of Models

As expected, models performed better with more data, whether it was from the pre-training phase or the local data collection phase. For local models, the average error in predictions dropped from around 0.5°C after four weeks of data to about 0.33°C after 32 weeks.

Fine-tuning the pre-trained models also improved their performance, especially when local data was limited. Interestingly, even without fine-tuning, the large data pre-trained model performed very well. In low-data situations, the small pre-trained model without adjustments performed similarly to a local model after just four weeks of data. However, with fine-tuning, the large pre-trained model achieved even better results.

#Variability in Performance

We observed that not all systems benefitted equally from the pre-trained models. The small pre-trained models showed significant differences in performance across various systems. While some could achieve predictions close to those made by better performing models, others struggled. This variance can arise from differences in how each system operates, even if they are identical in design.

A local model trained on its own data typically outperformed most small pre-trained models. However, there were instances where the small pre-trained models produced competitive results. This could be due to noise in the data or shifts in what the data represents over time.

#Implications for Energy Management

The findings in this research are important for future energy systems and for managing how data is used. A large pre-trained model that does not need fine-tuning can effectively control energy resources. This means local data might only be necessary for validation purposes, as it can help to maintain user privacy by ensuring that personal data remains on-site.

The use of pre-trained models offers an alternative to traditional machine learning methods by allowing for valuable insights without compromising privacy. With more interest in collecting data about energy use, especially behind-the-meter data, combining this data collection with pre-trained models could help alleviate privacy concerns.

#Conclusion and Future Directions

In conclusion, this study highlights that while pre-trained models can enhance predictions in energy management, careful selection and adjustment of these models are necessary to achieve the best results. Future research can investigate further the reasons behind differences in performance and how specific model adjustments can be made based on the data available.

Moving forward, it is crucial to consider how to value data contributions in monetary terms, particularly as energy markets evolve. The use of pre-trained models could play a significant role in shaping the future of energy management, ensuring that privacy is respected while still gathering valuable insights.