论文标题
深度学习制造业的短期即时能源消耗预测
Deep Learning for Short-term Instant Energy Consumption Forecasting in the Manufacturing Sector
论文作者
论文摘要
电力是一种波动的电源，需要短期和长期的精力计划和资源管理。更具体地说，在短期，准确的即时能源消耗中，预测极大地提高了建筑物的效率，为采用可再生能源提供了新的途径。在这方面，数据驱动的方法，即基于机器学习的方法，而不是更传统的方法，因为它们不仅提供了更简化的部署方式，而且还提供了最新的结果。从这个意义上讲，这项工作应用和比较了几种深度学习算法，LSTM，CNN，CNN-LSTM和TCN的性能，在制造业内的真实测试中。实验结果表明，TCN是预测短期即时能耗的最可靠方法。
Electricity is a volatile power source that requires great planning and resource management for both short and long term. More specifically, in the short-term, accurate instant energy consumption forecasting contributes greatly to improve the efficiency of buildings, opening new avenues for the adoption of renewable energy. In that regard, data-driven approaches, namely the ones based on machine learning, are begin to be preferred over more traditional ones since they provide not only more simplified ways of deployment but also state of the art results. In that sense, this work applies and compares the performance of several deep learning algorithms, LSTM, CNN, mixed CNN-LSTM and TCN, in a real testbed within the manufacturing sector. The experimental results suggest that the TCN is the most reliable method for predicting instant energy consumption in the short-term.
