Smart Information Technology Engineering Department, Kongju National University, Cheonan 31080, Republic of Korea.
Computer Engineering Department, Jeju National University, Jeju 63243, Republic of Korea.
Sensors (Basel). 2023 Mar 31;23(7):3640. doi: 10.3390/s23073640.
Home appliances are considered to account for a large portion of smart homes' energy consumption. This is due to the abundant use of IoT devices. Various home appliances, such as heaters, dishwashers, and vacuum cleaners, are used every day. It is thought that proper control of these home appliances can reduce significant amounts of energy use. For this purpose, optimization techniques focusing mainly on energy reduction are used. Current optimization techniques somewhat reduce energy use but overlook user convenience, which was the main goal of introducing home appliances. Therefore, there is a need for an optimization method that effectively addresses the trade-off between energy saving and user convenience. Current optimization techniques should include weather metrics other than temperature and humidity to effectively optimize the energy cost of controlling the desired indoor setting of a smart home for the user. This research work involves an optimization technique that addresses the trade-off between energy saving and user convenience, including the use of air pressure, dew point, and wind speed. To test the optimization, a hybrid approach utilizing GWO and PSO was modeled. This work involved enabling proactive energy optimization using appliance energy prediction. An LSTM model was designed to test the appliances' energy predictions. Through predictions and optimized control, smart home appliances could be proactively and effectively controlled. First, we evaluated the RMSE score of the predictive model and found that the proposed model results in low RMSE values. Second, we conducted several simulations and found the proposed optimization results to provide energy cost savings used in appliance control to regulate the desired indoor setting of the smart home. Energy cost reduction goals using the optimization strategies were evaluated for seasonal and monthly patterns of data for result verification. Hence, the proposed work is considered a better candidate solution for proactively optimizing the energy of smart homes.
家用电器被认为是智能家居能源消耗的主要部分。这是由于物联网设备的大量使用。各种家用电器，如加热器、洗碗机和吸尘器，每天都在使用。人们认为，对这些家用电器进行适当的控制可以减少大量的能源使用。为此，主要侧重于节能的优化技术被使用。当前的优化技术在一定程度上减少了能源的使用，但忽略了用户的便利性，这是引入家用电器的主要目标。因此，需要有一种优化方法，可以有效地解决节能和用户便利性之间的权衡问题。当前的优化技术应该包括除温度和湿度以外的天气指标，以有效地优化控制智能家居期望室内环境的能源成本。这项研究工作涉及到一种在节能和用户便利性之间进行权衡的优化技术，包括使用气压、露点和风速。为了测试优化，使用 GWO 和 PSO 混合方法建立了模型。这项工作涉及到使用家电能源预测来实现主动能源优化。设计了一个 LSTM 模型来测试家电的能源预测。通过预测和优化控制，可以主动有效地控制智能家居家电。首先，我们评估了预测模型的 RMSE 得分，发现所提出的模型产生的 RMSE 值较低。其次，我们进行了多次模拟，发现所提出的优化结果可以节省用于控制家电以调节智能家居期望室内环境的能源成本。使用优化策略的节能目标针对季节性和每月数据模式进行了评估，以验证结果。因此，所提出的工作被认为是主动优化智能家居能源的更好候选解决方案。
