# Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa
## 2 Related Work  
Based on a previous study, the current electrical load management and detection methods can be categorized into two categories: (1) regression model-based and (2) classification model-based. Different from the classification method that classifies the training data into different patterns to detect the data, the regression model-based method uses the data to learn the relationship of power consumption data with other variables or mine the intrinsic relationship of the data itself. After fitting the historical data, the model forecasts the future power consumption value. These prediction results serve as a baseline, and any data with large deviations from this line are identifiedas anomalies. By analyzing the relationship between the temperature and power usage of residents, [PERSON] et al. proposed a regression method-based anomaly detector [6]. Based on the view that the power consumption of users is affected by environmental factors, this work develops a novel approach to forecast the residential power load; however, the linear regression model used seems too simple to fit the complex load series well. [PERSON] et al. (2010) proposed a hybrid time series data prediction model, cascading ARIMA and Artificial neural networks (ANN) together for electrical load forecasting, which aims to address the prediction error of ARIMA associated with nonlinear regression [7]. This model combines the advantages of both linear and nonlinear models, which are widely used in the prediction of other time series data, such as temperature, stock, commodity prices and wind speed prediction [8; 9; 10; 11]. One problem of this hybrid model is that ARIMA is a statistical model. Previous works only show how to apply it with ANN, which improves the shortages of ARIMA in nonlinear regression without giving a reasonable parameter selection process and ignores the impact of parameter selection on the results. With the development of deep learning technology, many deep learning-based methods have been developed and applied in electrical load anomaly detection. [PERSON] et al. developed a framework for using a long short-term memory (LSTM) network to detect the consumer profile [12]. Compared with general regression model-based detection methods, the developed deep learning prediction model displayed high prediction accuracy; however, the training cost and interpretability were not discussed. The classification model-based method is another widely used method, which can be further classified into supervised classification model-based, unsupervised classification model-based and semi-supervised classification model-based methods. [PERSON] et al. proposed supervised classification-based anomaly detector targets to detect electricity thefts in advanced metering infrastructure [13]. In the training phase, to decide the optimal number of patterns in the training dataset, K-means and the silhouette score are employed. The advantage of the supervised classification model-based method is that its results are referable. The model groups the data into different patterns, and any data are screened out to belong to a malicious pattern. It can easy to conclude that it is an anomaly. However, this approach is limited by its high training cost and low feasibility. To overcome the limitation of the general supervised method in training cost and extend the feasibility of the classification model-based method, the unsupervised classification model is widely used in anomaly detection [5]. [PERSON] et al. developed an unsupervised classification-based building electrical load monitoring system [14]. In the data exploration phase, the spectral density estimation and decision tree are introduced to identify the dominant periods of each appliance. Then, by using an autoencoder model to calculate the anomaly score for each observation, the data with anomaly scores higher than the threshold are identified as potential threats. Targeting the detection of anomalies in the outputs of a solar power plant, [PERSON] et al.