Platform-Independent Web Application for Short-Term Electric Power Load Forecasting on 33/11 kV Substation Using Regression Tree

Abstract

Short-term electric power load forecasting is a critical and essential task for utilities in the electric power industry for proper energy trading, which enables the independent system operator to operate the network without any technical and economical issues. From an electric power distribution system point of view, accurate load forecasting is essential for proper planning and operation. In order to build most robust machine learning model to forecast the load with a good accuracy irrespective of weather condition and type of day, features such as the season, temperature, humidity and day-status are incorporated into the data. In this paper, a machine learning model, namely a regression tree, is used to forecast the active power load an hour and one day ahead. Real-time active power load data to train and test the machine learning models are collected from a 33/11 kV substation located in Telangana State, India. Based on the simulation results, it is observed that the regression tree model is able to forecast the load with less error.

Weather Forecasting Using Radial Basis Function Neural Network in Warangal, India

“…Additionally, the small difference between the training and validation losses indicates that the model is well trained without encountering underfitting or overfitting issues. The performance of the RBFNN model for temperature forecasting was validated by comparing it with the decision tree model [49], support vector regression [50], and elastic net regression [51] in terms of training and validation losses, as shown in Figure 9. From Figure 9, it is observed that the RBFNN model has lower validation losses, while the decision tree model exhibits signs of overfitting.…”

“…It can be accessed through the web link: https://sru-stlf-temp-humidity-rbf.streamlit.app/ (accessed on 12 March 2023). Temperature and humidity forecast obtained from different machine learning models, including the RBFNN, decision tree model [49], support vector regression [50], and elastic net regression [51], were compared with the corresponding actual values. As depicted in Figure 12 for temperature comparison and Figure 13 for humidity comparison, it can be observed that the forecast from the RBFNN model are closer to the actual values compared to the forecast from the other models.…”