# Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa
## 7 Conclusions  
To address the inherent limitation of the regression model-based detection method, this work proposed an improved scheme. While inheriting the advantages of the structure of the original model, both the performance of anomaly detection and prediction have been improved. From the perspective of load forecasting, the proposed predictor employs a moving window and an optimal regression model criterion to provide a prediction error feedback mechanism. This reduces the influence of the over- or underfitting problem in the real-time process. Through comparison with other advanced forecasting methods, it is proven that the proposed predictor has a simpler structure and better optimization effect and is more suitable for real-time prediction. Meanwhile, from the standpoint of anomaly detection, the proposed pattern matching-based anomaly detector is not only an independent but also a sample efficient detection method. By referring to a small amount of data, this method can effectively identify anomalies, save training cost, and improve the sample efficiency of the traditional classification model. Additionally, to simplify the operations and expand the application of machine learning technology, the proposed method is designed with visual display and function selection mechanisms, and it has low hardware requirements.  
At the end of this work, by exhibiting a comprehensive comparison result, the limitations, advantages, and practicability of each method in the application of real-time load forecasting and detection process have been discussed. This provides a reference value for follow-up research. The proposed method is believed to be used not only in off-grid rural areas but also in developed areas to improve the energy utilization rate and ensure the safety of the power grid.  
This work is our third study on electrical load anomaly detection of off-grid power plants in Africa. Our future work will also focus on comparing these different methods applied to various power plants. We hope to identify the best anomaly detection scheme for off-grid power plant loads as soon as possible and further improve the development of demand side management research.  
This work is designed, installed, tested, and completed by [PERSON], the first author of this article. The second author, [PERSON], and the corresponding author, [PERSON]-[PERSON] are responsible for supervision. Meanwhile, [PERSON] is also the project manager and the chief of the Innovative Technology and Energy Center. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the International S&T Cooperation Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (MSIP) (NRF-2017K1A3A9A04013801) and the Basic Research Lab Program through the NRF funded by the MSIT (2018R1A4A1059976).  
The authors [PERSON], [PERSON], and [PERSON] certify that they have NO affiliations with or involvement in any organization or entity with any financial interest, or non-financial in the subject matter or materials discussed in this manuscript.