In this paper short term load forecasting (STLF) is done with using multiple linear regression (MLR). A day ahead load forecasting is obtained in this paper. Regression coefficients were found out with the help of method of least square estimation. Load in electrical power system is dependent on temperature, due point and seasons and also load has correlation to the previous load consumption (Historical data). So the input variables are temperature, due point, load of prior day, hours, and load of prior week. To validate the model or check the accuracy of the model mean absolute percentage error is used and R squared is checked which is shown in result section. Using day ahead forecasted data weekly forecast is also obtained.

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With increase in advanced metering infrastructure and sensor systems there is increase in data collection. It is hard to handle a large amount of data and assure the quality of data. Good quality of data is essential in power system before taking decision. So data must be cleaned and filtered before operator takes any decision from the data. Otherwise it will cause hazardous condition if poor quality of data affects decision making without knowledge of operator. Bad Data detection and data cleaning is helpful to get over this risk. With use of MATLAB Bad Data can be easily detected. Bad Data can be also removed and Data filtering as well as Data smoothing is also possible. Data smoothing is necessary for some application ex. Load forecasting in power system. Here it is obtained by using Statistical techniques such as OWA (Optimally Weighted Average) and MA (Moving Average).