Despite the variety of sensors that can be used in a smart home or office setup, for monitoring energy consumption and assisting users to save energy, their usefulness is limited when they are not properly integrated into the daily activities of humans. Energy-saving applications in such environments can benefit from the use of sensors and actuators when data are properly fused with previous knowledge about user habits and feedback about current user preferences. In this article, we present an online recommender system implemented in the EM3 platform, a platform for Consumer Engagement Toward Energy-Saving Behavior. The recommender system uniquely fuses sensors’ data with user habits and user feedback and provides personalized recommendations for energy efficiency at the right moment. The user response to the recommendations directly triggers actuators that perform energy-saving actions and is recorded and processed for refining future recommendations. The EM3 recommendation engine continuously evaluates the three inputs (i.e. sensor data, user habits, user feedback) and identifies the micro-moments that maximize the need for the recommended action and thus the recommendation acceptance. We evaluate the efficiency of the proposed recommender system, which is based on a stacked-LSTM for fusing multi-sensor data streams, in several scenarios, and the observed accuracy on predicting the right moment to send a recommendation to the user ranged from 93% to 97%.

尽管可以在智能家居或办公室设置中使用各种传感器来监控能源消耗和帮助用户节约能源，但如果它们没有正确地融入人类的日常活动，它们的用处就会受到限制。当数据与先前有关用户习惯的知识和有关当前用户偏好的反馈正确融合时，此类环境中的节能应用可以受益于传感器和执行器的使用。在本文中，我们介绍了在 EM3 平台中实现的在线推荐系统，该平台是一个消费者参与节能行为的平台。推荐系统独特地将传感器数据与用户习惯和用户反馈相融合，并在适当的时候提供个性化的能源效率推荐。用户对推荐的响应直接触发执行节能动作的执行器，并被记录和处理以完善未来的推荐。EM3 推荐引擎不断评估三个输入（即传感器数据、用户习惯、用户反馈），并识别出最需要推荐动作的微时刻，从而最大限度地提高推荐接受度。我们评估了所提出的推荐系统的效率，该系统基于用于融合多传感器数据流的堆叠 LSTM，在几种情况下，观察到的预测正确时机向用户发送推荐的准确度为 93%到 97%。EM3 推荐引擎不断评估三个输入（即传感器数据、用户习惯、用户反馈），并识别出最需要推荐动作的微时刻，从而最大限度地提高推荐接受度。我们评估了所提出的推荐系统的效率，该系统基于用于融合多传感器数据流的堆叠 LSTM，在几种情况下，观察到的预测正确时机向用户发送推荐的准确度为 93%到 97%。EM3 推荐引擎不断评估三个输入（即传感器数据、用户习惯、用户反馈），并识别出最需要推荐动作的微时刻，从而最大限度地提高推荐接受度。我们评估了所提出的推荐系统的效率，该系统基于用于融合多传感器数据流的堆叠 LSTM，在几种情况下，观察到的预测正确时机向用户发送推荐的准确度为 93%到 97%。