Data-Driven Heat Pump Management: Combining Machine Learning with Anomaly Detection for Residential Hot Water Systems

TL;DR

This paper presents a novel approach combining machine learning and anomaly detection to optimize residential heat pump hot water systems, improving demand forecasting and operational efficiency based on household-specific data.

Contribution

It introduces a combined ML and anomaly detection framework for demand forecasting and adaptive control in residential heat pumps, validated on real household data.

Findings

LightGBM outperforms LSTM models in demand prediction

iForest achieves high anomaly detection accuracy with low false alarms

Proposed methods demonstrate robustness across diverse household patterns

Abstract

Heat pumps (HPs) have emerged as a cost-effective and clean technology for sustainable energy systems, but their efficiency in producing hot water remains restricted by conventional threshold-based control methods. Although machine learning (ML) has been successfully implemented for various HP applications, optimization of household hot water demand forecasting remains understudied. This paper addresses this problem by introducing a novel approach that combines predictive ML with anomaly detection to create adaptive hot water production strategies based on household-specific consumption patterns. Our key contributions include: (1) a composite approach combining ML and isolation forest (iForest) to forecast household demand for hot water and steer responsive HP operations; (2) multi-step feature selection with advanced time-series analysis to capture complex usage patterns; (3) application and tuning of three ML models: Light Gradient Boosting Machine (LightGBM), Long Short-Term Memory (LSTM), and Bi-directional LSTM with the self-attention mechanism on data from different types of real HP installations; and (4) experimental validation on six real household installations. Our experiments show that the best-performing model LightGBM achieves superior performance, with RMSE improvements of up to 9.37\% compared to LSTM variants with $R^2$ values between 0.748-0.983. For anomaly detection, our iForest implementation achieved an F1-score of 0.87 with a false alarm rate of only 5.2\%, demonstrating strong generalization capabilities across different household types and consumption patterns, making it suitable for real-world HP deployments.