@inproceedings{a9acd9a201eb430c8368b6f6e341ea26,
title = "Day ahead power demand forecasting for hybrid power at the edge",
abstract = "We describe the investigation and testing of univariate forecasting techniques on IoT hardware for application at “the Edge” using power demand forecasting. An evaluation of common forecasting techniques is presented, tested using the Morocco Buildings Electricity Consumption Datasets. An architecture is described for the Edge system that would enable 1-dayforward forecasts of power demand for use in provisioning power in a hybrid power system. Several of the configurations examined in this study performed comparably with current trends in forecasting methods and are suitable for this application at the Edge, providing a balance of performance and accuracy. A Long Short-Term Memory (LSTM) Neural Network configuration provided the most effective balance of performance, accuracy and simplicity of deployment that is desirable for an application at the Edge.",
author = "Calum McCormack and Christopher Wallace and Peter Barrie and Gordon Morison",
year = "2022",
month = jul,
day = "13",
doi = "10.1109/AIIoT54504.2022.9817155",
language = "English",
isbn = "9781665484541",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "437--441",
booktitle = "2022 IEEE World AI IoT Congress (AIIoT)",
address = "United States",
note = "World AI IoT Congress 2022, IEEE AIIoT 2022 ; Conference date: 06-06-2022 Through 09-06-2022",
}