# Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa
###### Abstract  
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. off-grid; sustainable energy development; load prediction; anomaly detection 2020 10 417 10.3390/app1024171 10.  
whether the current power usage is normal, and whether the system has any nanotechnology loss or potential dangers is the crucial function to ensure the security of the power plant; (2) real-time electrical load prediction. With the development of demand-side management, to reduce energy waste and improve the utilization rate of energy, meeting the real-time balance between power generation and consumption has become the most ideal situation of energy use [5]. Therefore, a comprehensive power management framework should not only detect the current power usage but also provide future power consumption information. Among all the machine learning algorithms applied in power girds, our previous study demonstrated the structure of the regression model-based power management method is a potential one that can satisfy power consumption forecasting and nanotechnology loss detection at the same time, providing an effective solution for improving the development of smart grids [5]. However, three intrinsic problems in this method are as follows: (1) considering the effectiveness of the energy arrangement system in time, and the diversity of human life affected on power consumption data, general prediction models may not be accurate enough to precisely forecast future power usage in real time; (2) given that the prediction result might be imprecise, it seems not reasonable to use it as the only reference to detect nontechnical losses; and (3) a comprehensive energy management system is expected to not only manage the system but also provide the results with appropriate references to explain. These limitations obstacle the application of this method. To address these gaps, this work proposes a new instructive off-grid power plant load management system that is based on a regression model; the method includes a real-time load predictor and a pattern matching-based load detector. The contributions of this study are as follows. 1. As the proposed predictor is employed to forecast the electrical load of an off-grid power plant applied in a rural area of Africa, to improve the prediction accuracy in real-time processes and reduce the training cost, the proposed predictor is combined with an autoregressive integrated moving average (ARIMA) approach and the Bayesian information criterion (BIC), where BIC is used to achieve automatic model optimization in each step. 2.