期刊
WASTE MANAGEMENT
卷 115, 期 -, 页码 8-14出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.wasman.2020.06.046
关键词
Forecast; Household waste generation; Multi-site; Deep learning; LSTM
资金
- Danish Council for Independent Research - Social Sciences
- Project 'Transportation issues related to waste management' [4182-00021]
Forecasting household waste generation using traditional methods is particularly challenging due to its high variability and uncertainty. Unlike studies that forecast waste generation at municipal or country levels, household data can present rapid short-term variations and highly non-linear dynamics. The aim of this paper is to investigate the advantages of using a state-of-the-art deep learning approach com-pared to traditional forecasting methods. We apply a multi-site Long Short-Term Memory (LSTM) Neural Network, to forecast waste generation rates from households using a long-term data base. The model is applied to historical data of weekly waste weights from households in the municipality of Herning, Denmark, in the period between 2011 and 2018. Results show that using a multi-site approach, instead of an individual fit for each household, can improve forecasting performance of the LSTM model by 28% on average, and that the LSTM approaches can effectively improve the results by 85% on average com-pared with traditional methods such as ARIMA. (C) 2020 Elsevier Ltd. All rights reserved.
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