Assessing Feature Importance for Short-Term Prediction of Electricity Demand in Medium-Voltage Loads

Abstract

The design of new monitoring systems for intelligent distribution networks often requires both real-time measurements and pseudomeasurements to be processed. The former are obtained from smart meters, phasor measurement units and smart electronic devices, whereas the latter are predicted using appropriate algorithms—with the typical objective of forecasting the behaviour of power loads and generators. However, depending on the technique used for data encoding, the attempt at making predictions over a period of several days may trigger problems related to the high number of features. To contrast this issue, feature importance analysis becomes a tool of primary importance. This article is aimed at illustrating a technique devised to investigate the importance of features on data deemed relevant for predicting the next hour demand of aggregated, medium-voltage electrical loads. The same technique allows us to inspect the hidden layers of multilayer perceptrons entrusted with making the predictions, since, ultimately, the content of any hidden layer can be seen as an alternative encoding of the input data. The possibility of inspecting hidden layers can give wide support to researchers in a number of relevant tasks, including the appraisal of the generalisation capability reached by a multilayer perceptron and the identification of neurons not relevant for the prediction task.