# Prediction Interval Adjustment for Load-Forecasting using Machine Learning
###### Abstract  
Electricity load-forecasting is an essential tool for effective power grid operation and 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 an understanding of not only load-point forecasts but also the uncertainty associated with it. In this paper, we develop a probabilistic load-forecasting method based on Association Rules and Artificial Neural Networks for Short-Term Load Forecasting (2 h ahead). First, neural networks are used to estimate point-load forecasts and the variance between these and observations. Then, using the latter, a simple prediction interval is calculated. Next, association rules are employed to adjust the prediction intervals by exploiting the confidence and support of the association rules. The main idea is to increase certainty regarding predictions, thus reducing prediction interval width in accordance to the rules found. Results show that the presented methodology provides a closer prediction interval without sacrificing accuracy. Prediction interval quality and effectiveness is measured using Prediction Interval Coverage Probability (PICP) and the Dawid-Sebastiani Score (DSS). PICP and DSS per horizon shows that the Adjusted and Normal prediction intervals are similar. Also, probabilistic and point-forecast Means Absolute Error (MAE) and Root Mean Squared Error (RMSE) metrics are used. Probabilistic MAE indicates that Adjusted prediction intervals fail by less than 2.5 MW along the horizons, which is not significant if we compare it to the 1.3 MW of the Normal prediction interval failure. Also, probabilistic RMSE shows that the probabilistic error tends to be larger than MAE along the horizons, but the maximum difference between Adjusted and Normal probabilistic RMSE is less than 6 MW, which is also not significant. prediction intervals; probabilistic electricity demand forecasting; association rules; artificial neural networks; machine learning +
Footnote †: journal: Applied  
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Footnote 1: _Applied_  
**Feartered Application: Prediction interval adjustment designed to be used in the Real-Time Electricity Market in Mexico. **  
**Feartered Application: Prediction interval adjustment designed to be used in the Real-Time Electricity Market in Mexico. based on electric demand forecasts, and the lack of accuracy in the estimations will lead to an inefficient decision-making process [2]. Specifically, the lack of accuracy may cause overestimation or underestimation of electricity demand [3]. The former causes an excessive amount of electricity to be purchased and supplied to the system, which causes power balance disturbances and instabilities in the power grid. The latter, on the other hand, leads to a risky operation of the power system by restraining the production of electricity, which may lead to load cuts directly affecting electricity users. In particular, short-term demand forecasting is highly difficult due to its requirement of quick response, the amount of information required, as well as its complexity. These models must take into consideration not only the electric consumption pattern of the region but also its regulatory requirements. For instance, the Mexican Electric Market (MEM) establishes that every short-term load forecast method must be capable of estimating 8 periods of 15 min ahead (2 h) [4]. Intending to improve the accuracy of load demand forecast, researchers have developed forecast methods for short-term, mid-term and long-term [5]. Mid-term and long-term load demand forecasting is closely related to planning activities (e.g., power system maintenance tasks and capacity expansion planning), whereas short-term forecasts are employed for the ongoing operation (e.g., everyday unit commitment).