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Anomaly detection in home power monitoring can be categorized into two main types: detection of electrical theft, leakage, or nontechnical loss and monitoring anomalies in the daily activities of residents. Focusing on the application and practicality of anomaly detection, we propose sample efficient home power anomaly detection (SEPAD) with improved monitoring performance in terms of electricity usage as well as changes in the daily living activities of residents via provision of detailed feedback. SEPAD consists of two classifiers: an appliance pattern matching classifier (APMC) and an energy consumption habit classifier (ECHC). The APMC uses a single-source separation framework based on a semi-supervised support vector machine (semi-SVM) model. This semi-supervised learning method requires only a small amount of labeled data to achieve high accuracy in near real time and is a sample efficient detection method. The hidden Markov model (HMM)-based ECHC improves the rationality of SEPAD by providing anomaly detection functionality with respect to the daily activities of householders, especially the elderly and residents in developing areas. When SEPAD detects the appearance of an unknown pattern or known patterns contrary to the household's electricity usage habits, it triggers an alarm. SEPAD was applied to monitor power consumption data from Mkalama, a rural area in Tanzania with 52 households containing nearly 150 occupants connected to a solar powered off-grid network. The results of the practical test demonstrate the high accuracy and practicality of the proposed method.

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To address the energy shortage problem in rural areas, significant attention has been paid to off-grid solar power plants. However, ensuring the security of these plants, improving the utilization rate of energy and, finally, proposing a sustainable energy development scheme for rural areas are still challenges. Under this, this work proposes a novel regression model-based stand-alone power plant load management system. This not only shows great potential in increasing load prediction in the real-time process but also provides effective anomaly detection for improving energy efficiency. The proposed predictor is a hybrid model that can effectively reduce the influence of fitting problems. Meanwhile, the proposed detector exhibits an efficient pattern matching process. That is, for the first time, a support vector machine (SVM) and the fruit fly optimization algorithm (FOA) are combined and applied to the field of energy consumption anomaly detection. This method was applied to manage the load of an off-grid solar power plant in a rural area in Tanzania with more than 50 households. In this paper, both the prediction and detection of our method are proven to exhibit better results than those of some previous works, and a comprehensive discussion on the establishment of a real-time energy management system has also been proposed.

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In the original publication the first author name is published incorrectly as ''Xin-Lin Wang''. The correct author name should be read as ''Xinlin Wang''.