An efficient approach for load forecasting in agricultural sector using machine learning

Abstract

The shortage of coal is increasing the risk of a power crisis in many states of India. In summer, the supply of electricity is less than the demand, due to which the government has to cut power. Such cuts can be avoided if the power demand is estimated correctly. It is essential to have an accurate forecast of the load, if this is not the case, then the power has to be cut, and in some places, the production is reduced. The agricultural sector is also affected by the power cuts. This paper focused on load forecasting in agriculture using machine learning and ensemble learning approaches. We first identified the various factors influencing the power load in the agriculture sector and then assessed the demand for electricity in this area. The agricultural electricity consumption data is collected from JVVNL. The results show that good forecasting accuracies are attained by including exogenous features.