Short-Term Load Forecasting in Distribution Substation Using Autoencoder and Radial Basis Function Neural Networks: A Case Study in India

Electric load forecasting is an essential task for Distribution System Operators in order to achieve proper planning, high integration of small-scale production from renewable energy sources, and to define effective marketing strategies. In this framework, machine learning and data dimensionality reduction techniques can be useful for building more efficient tools for electrical energy load prediction. In this paper, a machine learning model based on a combination of a radial basis function neural network and an autoencoder is used to forecast the electric load on a 33/11 kV substation located in Godishala, Warangal, India. One year of historical data on an electrical substation and weather are considered to assess the effectiveness of the proposed model. The impact of weather, day, and season status on load forecasting is also considered. The input dataset dimensionality is reduced using autoencoder to build a light-weight machine learning model to be deployed on edge devices. The proposed methodology is supported by a comparison with the state of the art based on extensive numerical simulations.