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From year to year, the poor economic conditions and lack of employment opportunities in villages are the main push factors for the rural population moves to the urban areas. Indonesia is home to more than 260 million people and is one of the world's most rapidly urbanizing countries. Between 1980 and 2010, Indonesia's urban population grew about fourfold, from 32.8 to 118.3 million. But when the rural population move to the urban area, they did not know about the cost of living in the urban area that is so much different from the rural area. Also, many rural populations get underpaid, and their wages is not enough to cover their cost of livings. This study aims to develop a regression model that is able to predict the cost of living of an area by using the Groceries Index and Restaurant Index with good accuracy. The developed regression model fulfills the residual assumption of heteroscedasticity by using the Weighted Least Squares method, which is selected after comparison with other regression equation by measuring its residual standard error value. The estimated Cost of Living Index could then be used accordingly to help determine the cost of living in a particular city (relative to New York City) or as factor in other calculations, such as calculating the minimum wage of a particular area. [ABSTRACT FROM AUTHOR]

In modern power systems, a clean database of historical electric loads is crucial for system analysis, planning studies, load forecasting, and decision making. To this end, the clean database should be free of anomalies while preserving the power consumption behavior of special events like holidays. In recent years, model-based anomaly detection methods have been successfully used for anomaly detection and correction in electrical load. In these methods, the collected load data are first compared with the predicted load obtained from a forecasting model; if the difference is greater than a specific limit, the collected load data are replaced with the predicted value. One of the disadvantages of these methods is the consideration of load profiles of the days with higher levels of difficulty in load forecasting, such as holidays, as anomalous load profiles and replacing them with predicted values, which results in fading some important characteristics of the load series. Therefore in this paper, a two-stage anomaly detection method is proposed to alleviate this problem. In the first stage, the wavelet transform is used to identify suspected load profiles. In the second stage, a robust regression method is used to further inspect the suspected load profiles and decide whether they are anomalies or not. By performing experiments on three datasets (a utility of Iran, New York City, and a North American utility) and comparing the results with four other methods, it's shown that the proposed method decreases false detection of anomalies while accurately correcting anomalous load profiles. Results of load forecasting with the Iranian dataset corrected by the proposed method showed an average of 2.28% and 1.58% error on the holidays and total days, respectively, which is less than the other four methods. [ABSTRACT FROM AUTHOR]