Meteier Quentin, El Kamali Mira, Angelini Leonardo, Abou Khaled Omar
HumanTech Institute, University of Applied Sciences and Arts Western Switzerland (HES-SO), 1700 Fribourg, Switzerland.
School of Management Fribourg, University of Applied Sciences and Arts Western Switzerland (HES-SO), 1700 Fribourg, Switzerland.
Sensors (Basel). 2023 Sep 19;23(18):7974. doi: 10.3390/s23187974.
Photovoltaic installations can be environmentally beneficial to a greater or lesser extent, depending on the conditions. If the energy produced is not used, it is redirected to the grid, otherwise a battery with a high ecological footprint is needed to store it. To alleviate this problem, an innovative recommender system is proposed for residents of smart homes equipped with battery-free solar panels to optimise the energy produced. Using artificial intelligence, the system is designed to predict the energy produced and consumed for the day ahead using three data sources: sensor logs from the home automation solution, data collected by the solar inverter, and weather data. Based on these predictions, recommendations are then generated and ranked by relevance. Data collected over 76 days were used to train two variants of the system, considering or without considering energy consumption. Recommendations selected by the system over 14 days were randomly picked to be evaluated for relevance, ranking, and diversity by 11 people. The results show that it is difficult to predict residents' consumption based solely on sensor logs. On average, respondents reported that 74% of the recommendations were relevant, while the values contained in them (i.e., accuracy of times of day and kW energy) were accurate in 66% (variant 1) and 77% of cases (variant 2). Also, the ranking of the recommendations was considered logical in 91% and 88% of cases. Overall, residents of such solar-powered smart homes might be willing to use such a system to optimise the energy produced. However, further research should be conducted to improve the accuracy of the values contained in the recommendations.
光伏装置在环境方面或多或少会有益处，这取决于具体条件。如果产生的能量未被使用，它会被重新导向电网，否则就需要一个生态足迹大的电池来储存能量。为缓解这一问题，针对配备无电池太阳能板的智能家居居民，提出了一种创新的推荐系统，以优化产生的能量。该系统利用人工智能，旨在通过三个数据源预测未来一天产生和消耗的能量：家庭自动化解决方案的传感器日志、太阳能逆变器收集的数据以及天气数据。基于这些预测，然后生成推荐并按相关性进行排序。考虑或不考虑能源消耗，使用76天收集的数据对系统的两个变体进行了训练。系统在14天内选择的推荐被随机抽取，由11人对其相关性、排序和多样性进行评估。结果表明，仅根据传感器日志很难预测居民的能耗。平均而言，受访者表示74%的推荐是相关的，而其中包含的值（即一天中的时间和千瓦能量的准确性）在66%（变体1）和77%的情况下是准确的。此外，在91%和88%的情况下，推荐的排序被认为是合理的。总体而言，此类太阳能智能家居的居民可能愿意使用这样一种系统来优化产生的能量。然而，应该进行进一步的研究，以提高推荐中包含的值的准确性。
