  Accurate building energy prediction is useful in various applications
starting from building energy automation and management to optimal storage
control. However, vulnerabilities should be considered when designing building
energy prediction models, as intelligent attackers can deliberately influence
the model performance using sophisticated attack models. These may consequently
degrade the prediction accuracy, which may affect the efficiency and
performance of the building energy management systems. In this paper, we
investigate the impact of bi-level poisoning attacks on regression models of
energy usage obtained from household appliances. Furthermore, an effective
countermeasure against the poisoning attacks on the prediction model is
proposed in this paper. Attacks and defenses are evaluated on a benchmark
dataset. Experimental results show that an intelligent cyber-attacker can
poison the prediction model to manipulate the decision. However, our proposed
solution successfully ensures defense against such poisoning attacks
effectively compared to other benchmark techniques.
