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Abstract：
Without a well-defined energy management plan, achieving meaningful improvements in human lifestyle becomes challenging. Adequate energy resources are essential for development, but they are both limited and costly. In the literature, several solutions have been proposed for energy management but they either minimize energy consumption or improve the occupant\'s comfort index. The energy management problem is a multi-objective problem where the user wants to reduce energy consumption while keeping the occupant\'s comfort index intact. To address the multi-objective problem this paper proposed an energy control system for a green environment called PMC (Power Management and Control). The system is based on hybrid energy optimization, energy prediction, and multi-preprocessing. The combination of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) is performed to make a fusion methodology to improve the occupant comfort index (OCI) and decrease energy utilization. The proposed framework gives a better OCI when compared with its counterparts, the Ant Bee Colony Knowledge Base framework (ABCKB), GA-based prediction framework (GAP), Hybrid Prediction with Single Optimization framework (SOHP), and PSO-based power consumption framework. Compared with the existing AEO framework, the PMC gives practically the same OCI but consumes less energy. The PMC framework additionally accomplished the ideal OCI (i-e 1) when compared with the existing model, FA-GA (i-e 0.98). The PMC model consumed less energy as compared to existing models such as the ABCKB, GAP, PSO, and AEO. The PMC model consumed a little bit more energy than the SOHP but provided a better OCI. The comparative outcomes show the capability of the PMC framework to reduce energy utilization and improve the OCI. Unlike other existing methodologies except for the AEO framework, the PMC technique is additionally confirmed through a simulation by controlling the indoor environment using actuators, such as fan, light, AC, and boiler.
摘要：
如果没有一个明确的能源管理计划，实现人类生活方式的有意义的改善变得具有挑战性。充足的能源资源对发展至关重要,但是它们既有限又昂贵。在文学中,已经提出了几种能量管理解决方案，但它们要么最小化能耗，要么提高乘员的舒适指数。能量管理问题是一个多目标问题，用户希望在保持乘员舒适指数不变的同时降低能耗。为了解决多目标问题，本文提出了一种用于绿色环境的能源控制系统，称为PMC（电源管理与控制）。该系统基于混合能源优化，能源预测，和多预处理。遗传算法（遗传算法）和粒子群优化（粒子群优化）的组合进行了融合方法，以提高乘员舒适指数（OCI）和降低能源利用率。与同行相比，拟议的框架给出了更好的OCI，蚂蚁蜂群知识库框架(ABCKB)，基于GA的预测框架(GAP)，采用单一优化框架的混合预测(SOHP)，和基于PSO的功耗框架。与现有的AEO框架相比，PMC给出几乎相同的OCI，但消耗更少的能量。与现有模型相比，PMC框架还实现了理想的OCI(i-e1)，FA-GA（i-e0.98）。与ABCKB等现有模型相比，PMC模型消耗的能量更少，GAP,PSO,和AEO。PMC模型比SOHP消耗更多的能量，但提供了更好的OCI。比较结果表明，PMC框架具有降低能源利用率和提高OCI的能力。与除了AEO框架之外的其他现有方法不同，PMC技术通过使用执行器控制室内环境，比如风扇,光,AC,和锅炉。
