**2. Challenges in Load Forecasting**

Load forecasting is a technique adopted by power utilities to predict the energy needed to meet generation to maintain grid stability. Data-gathering methods used in such an exercise are often unreliable, sometimes resulting in missing, nonsensical, out-of-range, and NaN(i.e., Not a Number) values. The presence of irrelevant and redundant information or noisy and unreliable data can affect knowledge discovery during the model training phase. The accuracy of forecasting is of great significance for both the operational and managerial loading of a utility. Despite many existing load forecasting methods, there are still significant challenges regarding demand load forecast accuracy. These challenges are data integrity verification, adaptive predictive model design, forecast error compensation, and dynamic model selection issues.
