@inproceedings{605a5b3fa21b468dabe2f6775ae128b0,

title = "Deep learning with LSTM networks for medium to long range demand forecasting: Comparison with machine learning benchmark model",

abstract = "Accurate load forecasting has become increasingly important as it can help power companies for better scheduling to reduce excessive electricity production. This study presents an approach that uses long short-term memory (LSTM) neural network to construct a forecasting model for medium to long term aggregate load forecasting. An array of linear and non-linear machine learning models are also built on electric load dataset and the best performing model is used as benchmark for comparison with deep learning approach. Weather related variables temperature, humidity and wind speed are used as exogenous inputs. The proposed methods are applied on a publicly available dataset of electric load consumption in metropolitan France. The goal is to ascertain with what accuracy can aggregated load values in Megawatts can be predicted using machine learning and deep learning approaches. Results show that LSTM based prediction model using only time lags gives low forecast errors and outperformed machine learning models optimized with hyperparameter tuning.",

keywords = "Deep learning, Long short-term memory (LSTM), Machine learning, Medium to long-term load forecasting, Recurrent neural network",

author = "Salah Bouktif and Ali Fiaz",

year = "2018",

month = jan,

day = "1",

language = "English",

series = "ECOS 2018 - Proceedings of the 31st International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems",

publisher = "University of Minho",

booktitle = "ECOS 2018 - Proceedings of the 31st International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems",

note = "31st International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2018 ; Conference date: 17-06-2018 Through 21-06-2018",

}