Efficient energy management is becoming increasingly complex as power systems integrate renewable sources, battery storage, and fluctuating electricity markets. To optimize energy usage, a system must dynamically decide whether to consume, store, buy, or sell electricity based on real-time conditions. The challenge was to develop predictive models that enhance an intelligent energy management system, ensuring optimal decisions are made based on demand forecasting, production trends, and market prices.
Traditional energy management systems rely on fixed rules or simple historical averages, limiting their ability to adapt to real-time changes in consumption patterns, renewable energy availability, and grid pricing. The client, a startup, sought to develop a solution that leverages machine learning for accurate forecasting and automated optimization. The goal was to maximize energy efficiency while allowing businesses to prioritize cost reduction or sustainability by minimizing CO2 emissions.

The solution was designed as a comprehensive machine learning-powered energy management system capable of almost real-time optimization. It consists of four core components working together to predict, decide, and act on energy flow dynamically. The first component is an electricity consumption forecasting model that analyzes historical usage data to predict demand fluctuations. The second is an energy production forecasting model, which estimates renewable energy generation based on historical trends and external factors such as weather conditions. These forecasting models were designed using a combination of machine learning and customized statistical methods tailored to work with short, specific time-series data.
The third component is an optimization module that makes real-time decisions on whether to use, store, buy, or sell electricity. It integrates forecasts from the first two modules, along with market electricity prices, to determine the most efficient energy distribution strategy. This allows businesses to balance financial costs with sustainability goals by choosing whether to optimize for minimal expenses or lower carbon emissions.
Finally, a model factory was developed to provide a scalable system for generating predictive models. This factory allows each client to have a customized energy management strategy by automatically building and fine-tuning machine learning models specific to their site configuration and energy usage patterns. By combining machine learning with flexible forecasting techniques, the system adapts to various installation types, ensuring robust and precise energy management across different use cases.

The project was successfully delivered on time and within budget, meeting the client’s expectations for performance and functionality. The system effectively optimizes energy usage, helping businesses reduce electricity costs while making informed, real-time decisions about energy storage and distribution. The ability to prioritize either financial savings or CO2 reduction provides flexibility for clients with different energy management goals.
Close collaboration with the client ensured that the project remained aligned with business priorities, speeding up development and enabling faster deployment. By integrating advanced predictive capabilities with optimization algorithms, the system has significantly improved energy efficiency, making it a valuable tool for businesses aiming to manage their electricity use more intelligently.
By leveraging advanced predictive modeling and real-time optimization, this system transforms energy management into a dynamic, intelligent process. Businesses can now make data-driven decisions that align with their financial and environmental priorities, paving the way for a more sustainable and cost-effective approach to electricity consumption.

The project began with defining precise goals and determining the necessary flexibility of the system. Site configurations were analyzed to strike a balance between adaptability and complexity, ensuring that the solution could be applied across different installations without unnecessary computational or algorithmical overhead. The time-to-market strategy was a key consideration in the development process to ensure a swift and effective deployment.
Choosing an appropriate weather data provider was critical for accurate energy generation forecasting, particularly for renewable sources. The system was built around three primary algorithms: one for forecasting energy consumption, another for forecasting energy generation, and a third for optimizing energy distribution. Model validation was conducted using real-world data from multiple installations to refine accuracy. The models were continuously fine-tuned and improved to enhance performance, ensuring reliable and efficient predictions. Finally, an application was developed for seamless integration into the client’s existing energy management infrastructure.

The forecasting models were developed using classical machine learning techniques, adapted for short and specific time series. Custom flexible statistical models were implemented to enhance the forecasting accuracy, ensuring reliable predictions even with limited historical data. The optimization algorithm was designed to analyze consumption, generation, and market pricing almost in real time, making autonomous decisions on energy flow to maximize efficiency.