Electricity demand forecasts are extremely important for energy suppliers and other actors in production, transmission, distribution and energy markets. Accurate models for predicting the load of electricity are critical to the operation and planning of a service company. load forecasts are extremely important for energy suppliers and other actors in production, transmission, distribution and energy markets. short-term load forecasts play an important role in the operation of power systems to ensure an immediate balance between energy production and demand. The accuracy of the prediction generated by the artificial intelligence has several factors, including, but not limited to, what is used to form the network algorithm, how much and which type of data are used in the network’s training set. In this research the best combination has been investigated of these factors to decrease performance parameters to give the best forecast possible.
Artificial intelligence techniques have gained importance in reducing estimation errors. Artificial neural network, Extreme Learning Machine and Decision tree such as LSBOOST and RF are among these artificial intelligence techniques. That are used in this research work for performance analysis. In this work, performance evaluation metrics such as MSE, RMSE, MAE and MAPE values are analysed and it is concluded that Random forest decision tree forecasting algorithm gives better performance of forecasting as compared to other artificial intelligence algorithms for 24 hours load forecasting as well as for 7 day load forecasting.
Visualization of Explainable Artificial Intelligence Techniques Using Variable Importance with Its Applications to Health Information Data
