Abstract
Electric power load forecasting is an essential task in the power system restructured environment for successful trading of power in energy exchange and economic operation. In this paper, various regression models have been used to predict the active power load. Model optimization with dimensionality reduction has been done by observing correlation among original input features. Load data has been collected from a 33/11 kV substation near Kakathiya University in Warangal. The regression models with available load data have been trained and tested using Microsoft Azure services. Based on the results analysis it has been observed that the proposed regression models predict the demand on substation with better accuracy.
Highlights
Electric power industries are seeking electric power prediction tools to forecast the load so that balance between load and generation can be maintained properly
Simple Linear Regression A total of 2160 samples have been considered in the dataset; out of these, 1728 samples have been used for training and the remaining 432 samples have been used for testing
Multiple Linear Regression A total of 2160 samples have been considered in the dataset; out of these, 1728 samples have been used for training and the remaining 432 samples have been used for testing of multiple linear regression (MLR) model
Summary
Electric power industries are seeking electric power prediction tools to forecast the load so that balance between load and generation can be maintained properly. Prediction of active power load is required for arranging regular interval activities and power firms are increasing their infrastructure [1]. Accurate load forecasting systems provide a better understanding of the dynamics of existing power systems [2]. Short-term active power load prediction is vital to effective power system service, such as dispatching power into the network to prevent regular power outages. Short term active power estimation is a critical prerequisite for optimal dispatch of generators in power plants [4]. Customers would be able to select a more cost-effective energy usage scheme if the short-term load forecasting methodology was more accurate. It helps the power system to reduce cost of power production and to utilize resources optimally [5]
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