Day-ahead prediction of plug-in loads using a long short-term memory neural network

The aim of this work is to develop and validate a miscellaneous electric loads (MEL) predictive model that does not require occupant-wise or building-wise model training nor model adaptation while achieving competitive accuracy. For that purpose, a long-short-term memory (LSTM) model was developed using monitored data from a research building located in Abu Dhabi, United Arab Emirates (UAE). In order to test the generalization capabilities of the proposed method, the model was evaluated using data from two additional buildings, a bank office building located in Frankfurt, Germany, and a university building in Ottawa, Canada. The results showed that the developed LSTM is applicable to the tested buildings without the need for occupant-wise or building-wise calibration, hence, addressing an important gap in the existing literature. In addition, a set of MEL predictive models from the literature, that are based on a Weibull distribution and Gaussian mixture models (GMM) are implemented and evaluated using the three identical data sets. The round-robin evaluation of existing MEL predictive models showed that the proposed LSTM model outperformed them especially when a combination of MEL and occupancy information was used as inputs. Finally, the neural network saturation was identified as the key challenge when developing an LSTM-based model for MEL prediction. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

The aim of this study is to develop and validate a MEL predictive model using LSTM that does not require occupant-wise or building-wise training, and the model is shown to have good generalization capabilities and outperform existing methods in terms of performance. The key challenge identified in developing an LSTM-based MEL prediction model is neural network saturation.