Energy Forecasting in Smart Power Net by Machine Learning Algorithms

Energy forecasting is a crucial function in a smart grid that enables grid operators to make knowledgeable decisions, optimize system performance, and guarantee the reliable and efficient delivery of electricity to consumers. In load forecasting, the demand for electricity within a particular area or over a specific time period is predicted based on historical data, weather pattern, and others. Global demand for power has greatly expanded as a consequence of the continually expanding world population. As a result of the inherent instability of energy consumption trends, the necessity for efficient energy management methods becomes critical. For the creation of optimization and control mechanisms, a very accurate and precise prediction of the long- and short-term energy consumption is required. To ensure that this requirement, distributed demand response algorithms and machine learning (ML) techniques are being used to more precisely estimate future energy consumption. Modern machine learning (ML) methods like neural networks (NNs), decision tree classifiers (DTC), logistic regression (LR), support vector machines (SVM), Naive Bayes (NB), and K-nearest neighbor (KNN) are all the subject of this study, which examines how well they perform. When compared to other algorithms, the LR and DTC produce relative improvements in outcomes. The proposed system is to forecast the load forecasting detection, use decision tree classifiers and logistic regression.