Abstract
This paper presents an approach to the energy management and control of the effective cost of energy in real-time electricity pricing environment. The strategy aims to optimise the overall energy flow in the electrical system that minimises the cost of power consumption from the grid. To substantiate these claims different cases of time-of-use (TOU) and renewable energy electricity tariff, i.e. in summer and winter seasons, and the robustness of system is analysed. A given energy demand for commercial usage in the city of Tshwane (South Africa) is used to investigate the behaviour of the designed method during low and high demand periods. As grid integrated renewable energy resources, photovoltaic (PV) is an important consideration in assuring excellent power supply and environmental issues in the commercial building. An adaptive optimal approach in the framework of model predictive control (MPC) is designed to coordinate the energy flow on the electrical system. The results show that the proposed adaptive MPC strategy can promote the new approach of an optimal electrical system design, which reduces the energy cost to pay the utility grid by about 46% or more depending on the set target.

Abstract
The comprehensive understanding of the residential electricity consumption patterns (ECPs) and how they relate to household characteristics can contribute to energy efficiency improvement and electricity consumption reduction in the residential sector. After recognizing the limitations of current studies (i.e. unreasonable typical ECP (TECP) extraction method and the problem of multicollinearity and interpretability for regression and machine learning models), this paper proposes an association rule mining based quantitative analysis approach of household characteristics impact on residential ECPs trying to address them together. First, an adaptive density-based spatial clustering of applications with noise (DBSCAN) algorithm is utilized to create seasonal TECP of each individual customer only for weekdays. K-means is then adopted to group all the TECPs into several clusters. An enhanced Apriori algorithm is proposed to reveal the relationships between TECPs and thirty five factors covering four categories of household characteristics including dwelling characteristics, socio-demographic, appliances and heating and attitudes towards energy. Results of the case study using 3326 records containing smart metering data and survey information in Ireland suggest that socio-demographic and cooking related factors such as employment status, occupants and whether cook by electricity have strong significant associations with TECPs, while attitudes related factors almost have no effect on TECPs. The results also indicate that those households with more than one person are more likely to change ECP across seasons. The proposed approach and the findings of this study can help to support decisions about how to reduce electricity consumption and CO2 emissions.

Abstract
Navigation through immersive virtual environments is a key concept for virtual reality as it allows users to explore those environments. Therefore, it is important to understand virtual reality navigation interfaces and their impact on the users' experience. This paper presents an objective performance evaluation of two types of navigation: natural (real walking and walk-in-place) vs. unnatural (gamepad). Steering Law was the objective performance metric chosen since it captures the relationship between the time to travel a path and the difficulty of that path. In addition to performance, subjective metrics were also considered, namely the feeling of presence, cybersickness and user satisfaction. The experiments consisted of having participants complete a series of paths with different indexes of difficulty and the time that a participant took to walk each path was measured. Overall results show that the navigation through real walking yielded better results when it comes to performance, cybersickness, and user satisfaction than the walk-in-place and gamepad navigation interfaces.

Abstract

Abstract
A silica resonator was demonstrated for random laser generation. The resonator consisted of a conventional microsphere fabricated in an optical fiber tip through electric arc discharge, and modifications to its geometry were carried out to create asymmetry inside the silica structure. The resulting Bunimovich stadium-like microsphere promotes multiple reflections with the boundaries, following the stochastic properties of dynamic billiards. The interference of the multiple scattered beams generates a random signal whose intensity was increased by sputter-coating the microstadium with a gold thin film. The random signal is amplified using an erbium-doped fiber amplifier (EDFA) in a ring cavity configuration with feedback, and lasing is identified as temporal and spectral random variations of the signal between consecutive measurements.

Abstract
The large-scale integration of photovoltaic generation (PVG) on distribution systems (DSs) preserving their technical constraints related to voltage fluctuations and active power (AP) flow is a challenging problem. Solar resources are accompanied by uncertainty regarding their estimation and intrinsically variable nature. This study presents a new probabilistic methodology based on quasi-static time-series analysis combined with the golden section search algorithm to integrate low and high levels of PVG into DSs to prevent AP flow in reverse direction. Based on the analysis of two illustrative case studies, it was concluded that the successful integration of PVG is not only related to the photovoltaic-cell manufacturing prices and conversion efficiency but also with the manufacturing prices of power electronic devices required for reactive power control.