Plug-in Electric Vehicle (PEV) user charging behavior has a significant influence on a distribution network and its reliability. Generally, monitoring energy consumption has become one of the most important factors in green and micro grids; therefore, predicting the charging demand of PEVs (the energy consumed during the charging session) could help to efficiently manage the electric grid. Consequently, three machine learning methods are applied in this research to predict the charging demand for the PEV user after a charging session starts. This approach is validated using a dataset consisting of seven years of charging events collected from public charging stations in the state of Nebraska, USA. The results show that the regression method, XGBoost, slightly outperforms the other methods in predicting the charging demand, with an RMSE equal to 6.68 kWh and R² equal to 51.9%. The relative importance of input variables is also discussed, showing that the user’s historical average demand has the most predictive value. Accurate prediction of session charging demand, as opposed to the daily or hourly demand of multiple users, has many possible applications for utility companies and charging networks, including scheduling, grid stability, and smart grid integration.

Residential energy consumption accounts for 21% of the total electricity use in the United States. Unfortunately, research indicates that almost 41% of this power is wasted. Changing the way that consumers use energy may be important in reducing home energy consumption. This paper looks at whether the implementation of certain real-time energy monitors has an impact on the residential rate of energy consumption in a metropolitan area with relatively low electricity rates. In the following case study, 151 Omaha residences were equipped with two variants of the Aztech In-Home Display (Aztech) as well as the Blue Line Power Cost Monitor (PCM) real-time energy monitors for a period of 16 months. The results of the data, 30 days after installation, revealed a statistically insignificant reduction of 12% in mean electrical consumption in houses equipped with a PCM and no reduction in mean consumption in homes using either variants of the Aztech device when compared to a randomly selected control sample. However, they proved effective in the short term if utilized by utilities for mass distribution to foster awareness among participating residents of their own patterns of residential electricity consumption and on the environmental impacts of energy saving.

Multicell battery has been widely used in various electrical and electronics devices. To achieve the optimal multicell battery design, accurately modeling battery discharge behavior of multicell battery is crucial. However, modeling discharge behavior of multicell battery is very challenging due to the nonlinear battery effects, nonuniform cell quality, and various cell connections. In this paper, we develop a circuit-based multicell battery model to accurately model the multicell battery discharge behavior in terms of available capacity, output voltage, and internal resistance with consideration of nonlinear battery effect and nonuniform cell quality. It also characterizes the cell discharge current distribution of cell strings in parallel connection. The proposed multicell battery model has been validated by extensive simulation and experimental results under various load conditions.

Buildings currently account for 30–40 percent of total global energy consumption. In particular, commercial buildings are responsible for about 12 percent of global energy use and 21 percent of the United States’ energy use, and the energy demand of this sector continues to grow faster than other sectors. This increasing rate therefore raises a critical concern about improving the energy performance of commercial buildings. Recently, researchers have investigated ways in which understanding and improving occupants’ energy-consuming behaviors could function as a cost-effective approach to decreasing commercial buildings’ energy demands. The objective of this paper is to present a detailed, up-to-date review of various algorithms, models, and techniques employed in the pursuit of understanding and improving occupants’ energy-use behaviors in commercial buildings. Previous related studies are introduced and three main approaches are identified: (1) monitoring occupant-specific energy consumption; (2) Simulating occupant energy consumption behavior; and (3) improving occupant energy consumption behavior. The first approach employs intrusive and non-intrusive load-monitoring techniques to estimate the energy use of individual occupants. The second approach models diverse characteristics related to occupants’ energy-consuming behaviors in order to assess and predict such characteristics’ impacts on the energy performance of commercial buildings; this approach mostly utilizes agent-based modeling techniques to simulate actions and interactions between occupants and their built environment. The third approach employs occupancy-focused interventions to change occupants’ energy-use characteristics. Based on the detailed review of each approach, critical issues and current gaps in knowledge in the existing literature are discussed, and directions for future research opportunities in this field are provided.