This paper introduces a dynamic Smart Home Energy Management System (SHEMS) integrating a hybrid photovoltaic (PV) and gravity energy storage (GES) system aimed at minimizing environmental impacts and household energy consumption. The novel SHEMS features a one-week dynamic forecasting model that adapts to variable electricity prices, smart appliance schedules, solar output, and energy storage states. These results demonstrate that the system not only reduces household energy usage but also cuts electricity bills significantly, supplying power autonomously for up to 8.5 hours daily. By leveraging real-time data from the Dark Sky API on cloud cover and temperature, this model accurately predicts solar radiation and PV generation, aligning it with both grid and residential demands. The forecasting accuracy was assessed using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), which improved to 12.55% and 4.91% respectively, from initial values of 22.46% for RMSE and 11.78% for MAPE. These advancements enhance grid stability and optimize energy storage during peak periods, reducing dependence on fossil fuels. The integration of innovative renewable energy technologies and sophisticated forecast modeling significantly boosts the system's efficiency, promoting the sustainable use of energy resources in line with environmental and economic goals.

中文翻译：

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具有并网管理的智能家居的混合光伏和重力储能集成


本文介绍了一种动态智能家居能源管理系统 （SHEMS），该系统集成了混合光伏 （PV） 和重力储能 （GES） 系统，旨在最大限度地减少对环境的影响和家庭能源消耗。新颖的 SHEMS 具有为期一周的动态预测模型，可适应可变的电价、智能电器时间表、太阳能输出和储能状态。这些结果表明，该系统不仅减少了家庭能源的使用，还显着降低了电费，每天自主供电长达 8.5 小时。通过利用来自 Dark Sky API 的云量和温度实时数据，该模型可以准确预测太阳辐射和光伏发电，使其与电网和住宅需求保持一致。使用均方根误差 （RMSE） 和平均绝对百分比误差 （MAPE） 评估预测准确性，分别从 RMSE 的初始值 22.46% 和 MAPE 的 11.78% 提高到 12.55% 和 4.91%。这些进步增强了电网稳定性并优化了高峰期的储能，从而减少了对化石燃料的依赖。创新的可再生能源技术和复杂的预测建模的集成显着提高了系统的效率，促进了能源资源的可持续利用，符合环境和经济目标。