Multi-site household waste generation forecasting using a deep learning approach

Abstract

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 compared 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 compared with traditional methods such as ARIMA.

Keywords: Deep learning; Forecast; Household waste generation; LSTM; Multi-site.