Business Challenge 

The production of renewable energy sources — such as solar power — is highly variable, depending on the time of day and weather conditions. This unpredictability presents a fundamental challenge, as a consumer’s energy demand rarely aligns with the amount of energy they are generating at any given moment. As a result, surplus energy often goes unused during the day, while at other times, costly energy shortages must be compensated from the grid. 

The answer to this challenge lies in the creation of energy communities, where members can share energy with one another. However, this introduces a new and complex management problem: how can energy flows within the community, energy storage, and grid trading be optimized in the most efficient and cost-effective way? Addressing this requires an intelligent data platform capable of making automated, optimal decisions using real-time data, market prices, and AI-driven forecasting. 

Solution 

At the core of the system lies the centralized and secure management of IoT metering devices installed at the community’s measurement points. Through a web-based interface, the platform enables operators to register devices, monitor communication, and track incoming data in real time. 

The true business value of the solution lies in the intelligent processing of raw data and the economic benefits derived from it. Data collected from the IoT devices passes through distinct logical layers — raw (Bronze), cleansed (Silver), and business-ready (Gold) — transforming it into a reliable, high-quality source of information. 

A key element of the data processing pipeline is the application of Machine Learning. The system employs an advanced LSTM (Long Short-Term Memory) neural network model to forecast the expected energy consumption and production of community members. In addition to historical data series, the model also incorporates external factors such as detailed meteorological forecasts — including temperature, cloud coverage, and solar radiation. The models are automatically retrained each week using the latest data, ensuring that forecasting accuracy remains consistently high over time. 

The generated forecasts serve as input for an optimization engine that determines the optimal operational strategy for the community’s energy storage units (batteries) over the next 24 hours. This component applies a complex mathematical model and advanced optimization algorithms to achieve the most efficient and cost-effective energy management within the community. 

Results 

The Energy Community Data Collection Application Server is a complex intelligent energy management solution built on Azure cloud components. It enables energy providers to efficiently control distributed energy generation and storage assets. The system empowers energy communities to maximize economic benefits by automating and optimizing the operation of storage units based on market prices and local energy demands.