@inproceedings{10.1145/3307772.3330173,
author = {Ali, Sarwan and Mansoor, Haris and Arshad, Naveed and Khan, Imdadullah},
title = {Short Term Load Forecasting Using Smart Meter Data},
year = {2019},
isbn = {9781450366717},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3307772.3330173},
doi = {10.1145/3307772.3330173},
abstract = {Accurate short term electricity load forecasting is crucial for efficient operations of the power sector. Predicting loads at a fine granularity (e.g. households) is made challenging due to a large number of (known or unknown) factors affecting power consumption. At larger scales (e.g. clusters of consumers), since the inherent stochasticity and fluctuations are averaged out, the problem becomes substantially easier. In this work we propose a method for short term (e.g. hourly) load forecasting at fine scale (households). Our method use hourly consumption data for a certain period (e.g. previous year) and predict hourly loads for the next period (e.g. next 6 months). We do not use any non-calendar information, hence our technique is applicable to any locality and dataset. We evaluate effectiveness of our technique on three benchmark datasets from Sweden, Australia, and Ireland.},
booktitle = {Proceedings of the Tenth ACM International Conference on Future Energy Systems},
pages = {419–421},
numpages = {3},
keywords = {Data Transformation, SVD, Clustering, Load Forecasting},
location = {Phoenix, AZ, USA},
series = {e-Energy '19}
}