A methodology for prediction interval adjustment for short term load forecasting

Electricity load forecasting is an essential tool for the effective power grid operation and for energy markets. However, the lack of accuracy on the estimation of the electricity demand may cause an excessive or insufficient supply which can produce instabilities in the power grid or cause load cuts. Hence, probabilistic load forecasting methods have become more relevant since these allow to understand, not only load point forecasts but also the uncertainty associated with it. In this thesis, a framework to generate prediction models that generate prediction intervals is proposed. This framework is designed to create a probabilistic STLF model by completing a series of tasks. First, prediction models will be generated using a prediction method and a segmented time series dataset. Next, prediction models will be used produce point forecast estimations and errors will be registered for each subset. At the same time, an association rules analysis will be performed in the same segmented time series dataset to model cycling patterns. Then, with the registered errors and the information obtained by the association rules analysis, the prediction intervals are created. Finally, the performance of the prediction intervals is measured by using specific error metrics. This methodology is tested in two datasets: Mexico and Electric Reliability Council of Texas (ERCOT). Best results for Mexico dataset are a Prediction Interval Coverage Probability (PICP) of 96.49% and Prediction Interval Normalized Average Width 12.86, and for the ERCOT dataset a PICP of 94.93% and a PINAW of 3.6. These results were measured after a reduction of 14.75% and 5.25% in the prediction intervals normalized average width of the Mexico and ERCOT dataset respectively. Reduction of the prediction interval is important because it can helps in reducing the amount of electricity purchase, and reducing the electricity purchase even in 1% represents a large amount of money. The main contributions of this work are: a framework that can convert any point forecast model in a probabilistic model, the Max Lift rule method for selection of high quality rules, and the metrics probabilistic Mean Absolute Error and Root Mean Squared Error.

https://orcid.org/0000-0002-8934-5632