*3.4. Error Correction Model*

The predictive framework proposed in this study is designed for short-term and long-term stochastic load forecasts. The framework is based on feature characteristics of historical data. However, the state characteristics of the model features may change because of uncertainties in feature variable predictions or contingencies. Thus, for each parametric model, an error correction model is defined to cater for irregularities with the forecast due to sudden changes in the feature parameters. The error correction model (ECM) is dedicated to estimating the deviations from long-term estimates to influence short-term forecasts. The ECM is defined to compensate for three error types: variance, permanent bias, and temporary bias of the prediction models. Two of the ECMs (i.e., variance and permanent bias) deal with K-means predictions, whereas the temporary bias ECM caters to ANN prediction irregularities.
