Energy

Unlock smart meter load profiles to accelerate the energy transition

Energy Efficiency.

The energy grid is under pressure. Good cooperation between different parties is critical. Herein lie opportunities for providers of innovative energy services. This is especially true as significant electrification is taking place: reducing gas reliance (heat pumps), reducing gasoline reliance (EVs), and heating in the Netherlands (air conditioners). 

 

Therefore, a smart measurement and control system has been added to this form of electricity supply (smart grids). This system measures energy peaks and power dips and ensures energy is supplied or redistributed in a streamlined manner.

Challenges.

Mathematical models are often used to understand energy requirements. Their performance is highly dependent on the quality and amount of data available for training. However, collecting sufficient high-quality data is time-consuming and extremely expensive. 


Cooperation between energy producers, suppliers, grid operators, and Independent Service Providers (ISPs) is essential, but using third-party data is problematic. After all, energy consumption is privacy-sensitive data, so there are many restrictions regarding its usage. Processing it can lead to costly fines from privacy authorities and increases the risk of reputational damage from data breaches. The use of synthetic data offers a solution.

… Energy consumption is privacy sensitive data

… Gathering a sufficient amount of high-quality electricity data is incredibly costly and time-consuming

Use cases

Predicting energy consumption with what-if scenarios

Better estimation of energy consumption

Suppliers can predict power demand. For example, using consumption and user profiles based on house types. But also, by conditioning the data. For example, how does a temperature drop of 10 degrees impact energy consumption? Or if the number of electric cars in a region increases by 20 percent?

Predicting infrastructure maintenance

Realistic models with essential basic data

Utilities face high recurring costs. Many energy companies would like AI to perform tasks such as predicting maintenance. However, there are too few data sources for this. The available data may not be used, or only to a limited extent, because it is privacy-sensitive data. However, the models require input from data with examples of failures or incidents. BlueGen.ai helps to supplement the missing data. Thus, utilities can still have better predictive models.

Sharing data in accordance with privacy laws

Introducing new smart energy services

Independent Service Providers (ISPs) are essential in making energy regulation efficient. They develop and test new smart energy services. Data sharing happens in a privacy-safe way. Energy Data Services Netherlands (EDSN), which provides certificates to ISPs, has high technical requirements and standards for handling privacy-sensitive data. Synthetic data can ensure ISPs comply fully in this manner.

Fraud Detection

Improved fraud detection and theft prevention thanks to more efficient models

Approximately $95 billion worth of electricity theft is estimated annually worldwide. Fraud detection with AI requires sufficient data. BlueGen.ai provides data pre-processing and data synthesis to refine machine learning for this purpose. Synthetic training sets are also better than real data. By balancing the dataset, the model can detect irregularities better and faster.

Research

Access to data and higher-quality research on new energy solutions

Solving energy challenges requires research from universities and research institutions. Conducting good research requires representative data. This must often be kept private due to privacy concerns. Thanks to BlueGen.ai, we can share this data. The research is even conducted faster and of higher quality.

Customer profiling and segmentation

New commercial opportunities thanks to customized rates and services

Using BlueGen.ai's platform combined with synthetic data, you can generate customer tax curves. It is possible to create curves according to different temperature scenarios based on household characteristics, among other things. These contribute to the development of personalized rates and other energy products for diverse households.