Providing recommendation for energy-efficiency at smart homes

Energy cost reduction is a challenging task for smart home users. Many state-of-the-art techniques achieve it by appliance curtailment, off-peak scheduling, peak load shaving, energy prediction, user activity-based irrelevant appliance detection, and energy resource scheduling. Despite vast research, there is still a broad scope to investigate efficient ways to reduce energy costs. To this end, this study proposes a novel technique that monitors household appliances in real-time according to user-devised criteria that comprise an appliance monitor time and a threshold for maximum energy usage. The proposed technique provides off-peak energy scheduling recommendations when an appliance satisfies the user criteria. Thus, the user can schedule appliances to any off-peak hour specified in the price signal ensuring maximum user comfort by personal choices. The off-peak scheduling entirely depends on the price signal as it includes the distribution of peak, mid-peak, and off-peak hours with their respective prices. We investigate the performance of our proposed technique with the time-of-use and critical peak pricing signals. We perform several experiments on two publicly available energy consumption datasets with four price signals to demonstrate the proposed technique's performance and energy cost reduction capability. The energy cost-saving is achieved by off-peak scheduling, keeping the total energy consumption unchanged during the experiments. For the investigated datasets, the simulation results demonstrate a significant cost-saving performance of up to 84%. Various off-peak scheduling techniques use different datasets and parameters settings to validate the effectiveness of their technique. To compare the energy-saving performance of different off-peak scheduling algorithms, we propose a novel evaluation metric that computes the performance on similar criteria. The performance of different techniques calculated with a new evaluation metric reveals that our technique's performance is better than other state-of-the-art techniques. Overall, off-peak scheduling minimizes the peak load on smart grids, yielding further cost-effective energy tariffs and a balanced energy supply for all stakeholders.