Figure 4
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Energy consumption in residential sector is the 25% of all the sectors. Maintaining user comfort and energy optimization are the major tasks of Home Energy Management System. Appliances of Heating, Ventilation and Air Conditioning (HVAC) and lighting devices constitute up to 64% and 4% of energy consumption respectively in residential buildings. Di...
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The increasing load demand in residential area and irregular electricity load profile encouraged us to propose an efficient Home Energy Management System (HEMS) for optimal scheduling of home appliances. We propose a multi-objective optimization based solution that shifts the electricity load from On-peak to Off-peak hours according to the defined...
Citations
... It can make the household microgrid beneficial to not only the HEMS users but also the public utilities [110]. There are six different strategies of load profiling which are peak shaving, valley filling, strategic conservation, load shifting, strategic growth, and flexible load shape [13], [98], [111], [112] Compared with the first three strategies, load shifting, strategic growth, and flexible load shape provide more systematic and large-scale changes in load management. Table 6 represents the objective function of the load profiling, which mainly includes load peak minimization [98], [113], peak-to-average ratio (PAR) reduction [91], [103], [114], self-consumption [106] and energy balance [115]. ...
Growing electricity demand, the deployment of renewable energy sources and the widespread use of smart home appliances provide new opportunities for home energy management systems (HEMSs), which can be defined as systems that improve the overall energy production and consumption of residential buildings by controlling and scheduling the use of household equipment. By saving energy, reducing residential electricity costs, optimizing the utilization rate and reliability of utility companies’ power systems, and reducing air pollution for society, HEMSs lead to an enhancement in the socioeconomic development of low-carbon economies. This review aims to systematically analyze and summarize the development trends and challenges of HEMSs in recent years. This paper reviews the development history of the HEMS architecture and discusses the characteristics of several major communication technologies in the current HEMS infrastructure. In addition, the common objectives and constraints related to scheduling optimization are classified, and several optimization methods in the literature, including various intelligent algorithms, have been introduced, compared, and critically analyzed. Furthermore, experimental studies and challenges in the real world are also summarized and recommendations are given. This paper reveals the trend from simple to complex in the architecture and functionality of HEMSs, discusses the challenges for future improvements in modeling and scheduling, and shows the development of various modeling and scheduling methods. Based on this review, researchers can gain a comprehensive understanding of current research trends in HEMSs and open up ideas for developing new modeling and scheduling approaches by gaining insight into the trade-offs between optimum solutions and computational complexity.
Model predictive control (MPC) is an advanced control technique. It has been deployed to harness the energy flexibility of a building. MPC requires a dynamic model of the building to achieve such an objective. However, developing a suitable predictive model is the main challenge in MPC implementation for flexibility activation. This study focuses on the application of key performance indicators (KPIs) to evaluate the suitability of MPC models via feature selection. To this end, multiple models were developed for two houses. A feature selection method was developed to select an appropriate feature space to train the models. These predictive models were then quantified based on one-step ahead prediction error (OSPE), a standard KPI used in multiple studies, and a less-often KPI: multi-step ahead prediction error (MSPE). An MPC workflow was designed where different models can serve as the predictive model. Findings showed that MSPE better demonstrates the performance of predictive models used for flexibility activation. Results revealed that up to 57% of the flexibility potential and 48% of energy use reduction are not exploited if MSPE is not minimized while developing a predictive model.
