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.
Highlights
Because of the general poverty in the rural areas of Africa, the most common pattern of settlement is small villages that are unable to access electricity [1,2]
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 manage the system and provide the results with appropriate references to explain
We previously previously built three off-grid solar power plants in different. Considering that these three off-grid power supply systems need to operate in harsh harsh environments environments with limited skilled manpower for operation and maintenance, a power plant remote monitoring skilled manpower operation maintenance, monitoring system implemented in each village to realize remote data transmission and control system was wasdesigned designedand and implemented in each village to realize remote data transmission and from
Summary
Because of the general poverty in the rural areas of Africa, the most common pattern of settlement is small villages that are unable to access electricity [1,2]. 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 manage the system and provide the results with appropriate references to explain These limitations obstacle the application of this method.
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