Predict+ Machine Learning Distributed Energy BI
Optimizing Distributed Energy Systems with AI-Driven Solar Energy Forecasting for FSight
problem
Our client FSight, a rapidly growing startup, operates in the evolving energy market, shifting towards distributed grids and peer-to-peer energy trading. With the transition from centralized power plants, they encountered a challenge in managing distributed energy systems efficiently. FSight sought to optimize energy flow for end-users, service providers, and grid operators using Artificial Intelligence to reduce energy costs.
problem
Our client FSight, a rapidly growing startup, operates in the evolving energy market, shifting towards distributed grids and peer-to-peer energy trading. With the transition from centralized power plants, they encountered a challenge in managing distributed energy systems efficiently. FSight sought to optimize energy flow for end-users, service providers, and grid operators using Artificial Intelligence to reduce energy costs.
solution
To address FSight's needs, we provided analytics and prediction services to optimize electricity usage and enhance existing photovoltaic (PV) forecasting algorithms. Our solution focused on predicting solar energy production to aid in cost optimization. Leveraging business intelligence services, FSight could forecast energy production, prices, and make real-time consumption and storage decisions. Additionally, our solution facilitated peer-to-peer or peer-to-grid energy trading, empowering FSight to adapt to the dynamic energy market.
solution
To address FSight's needs, we provided analytics and prediction services to optimize electricity usage and enhance existing photovoltaic (PV) forecasting algorithms. Our solution focused on predicting solar energy production to aid in cost optimization. Leveraging business intelligence services, FSight could forecast energy production, prices, and make real-time consumption and storage decisions. Additionally, our solution facilitated peer-to-peer or peer-to-grid energy trading, empowering FSight to adapt to the dynamic energy market.
results
As a result of our collaboration, we developed machine learning-based models to predict energy production for solar panels across various stations, increasing the complexity of forecasting compared to single-station predictions. Initially, we conducted an introductory audit of all data, leading to the development of the XGBoost model based on historical tabular data. Integration of this model into FSight's existing platform was challenging due to the Java infrastructure. Subsequently, we developed a clustering-based model, providing more stable predictions and reducing errors. Integrating these models into the system, along with existing ones, led to a significant reduction in error. Additionally, we built a pipeline to collect statistics on all stations, ensuring the models' correct operation and efficiency..
results
As a result of our collaboration, we developed machine learning-based models to predict energy production for solar panels across various stations, increasing the complexity of forecasting compared to single-station predictions. Initially, we conducted an introductory audit of all data, leading to the development of the XGBoost model based on historical tabular data. Integration of this model into FSight's existing platform was challenging due to the Java infrastructure. Subsequently, we developed a clustering-based model, providing more stable predictions and reducing errors. Integrating these models into the system, along with existing ones, led to a significant reduction in error. Additionally, we built a pipeline to collect statistics on all stations, ensuring the models' correct operation and efficiency..
tech stack
tech stack
