automation data - 2019
Optimizing Prosumer Battery Storage with Machine Learning
IEEE research project on ANN-based household load forecasting and linear optimization for battery control under dynamic electricity pricing.
Key Result
Annual revenue per household battery — averaged across 38 representative households
The ML controller improved revenue in every one of the 38 evaluated households, with gains between 3% and 21% over the rule-based baseline.
Improvement over baseline
+12%
average across all households
Households evaluated
38
selected from 2,486 load profiles
Gap to perfect foresight
10 EUR/yr
remaining optimization potential
How it works
Forecast demand
An ANN predicts household load for the next 24 hours using historical patterns.
Optimize scheduling
A linear optimizer plans charge/discharge decisions using price signals and the forecast.
Execute with feedback
Hourly battery control adjusts for the real state of charge at execution time.
Dynamic pricing signal
Day-ahead spot prices guide when to store vs. export — the rule-based controller ignores this.
Research Question
How much better can residential battery storage be managed when the controller does not only react to the current moment, but also anticipates household load and dynamic prices?
The paper compares a classic rule-based battery controller with an ML-supported control stack and a perfect-foresight benchmark.
System Design
The ML approach forecasts household demand with an artificial neural network and feeds the forecast into a 24-hour linear optimization model.
That optimization proposes charge, discharge, grid import, and feed-in decisions which are then converted into hourly battery control using rule logic and the real state of charge.
Dataset And Model
The evaluation combines 2,486 cleaned ISSDA household load profiles, dynamic day-ahead pricing, Osnabrueck PV generation data from an 8 kW rooftop system, and a 5 kWh battery with 81% round-trip efficiency.
For evaluation, 38 representative households were selected across the cluster structure of the dataset and simulated over a full year of hourly decisions.
Outcome
Average annual revenue increased from 117 EUR/year with rule-based control to 130 EUR/year with the ML-supported controller, while the perfect-foresight benchmark reached 140 EUR/year.
Across the 38 evaluated households, the ML controller improved revenue in every case and delivered gains between 3% and 21%, with a 12% average uplift.