Department of Economics and Business Economics, Aarhus University, Fuglesangs allé 4, DK-8210 Aarhus V, Denmark.
Waste Manag. 2020 Sep;115:8-14. doi: 10.1016/j.wasman.2020.06.046. Epub 2020 Jul 21.
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.
由于其高度的可变性和不确定性，使用传统方法预测家庭垃圾产生量特别具有挑战性。与预测市或国家层面垃圾产生量的研究不同，家庭数据可能会呈现出快速的短期变化和高度非线性动态。本文旨在探讨使用最先进的深度学习方法相对于传统预测方法的优势。我们应用一种多站点长短时记忆（LSTM）神经网络，使用长期数据库来预测家庭的垃圾产生率。该模型应用于丹麦赫宁市家庭每周垃圾重量的历史数据，时间跨度为 2011 年至 2018 年。结果表明，使用多站点方法（而不是为每个家庭单独拟合）可以将 LSTM 模型的预测性能平均提高 28%，并且 LSTM 方法可以将结果平均提高 85%，与 ARIMA 等传统方法相比。
