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Accurate and reliable low-voltage load forecasting is critical to optimal operation and control of distribution network and smart grid. However, compared to traditional regional load forecasting at high-voltage level, it faces tough challenges due to the inherent high uncertainty of the low-capacity load and distributed renewable energy integrated in the demand side. This paper proposes a novel hybrid ensemble deep learning (HEDL) approach for deterministic and probabilistic low-voltage load forecasting. The deep belief network (DBN) is applied to low-voltage load point prediction with the strong ability of approximating nonlinear mapping. A series of ensemble learning methods including bagging and boosting variants are introduced to improve the regression ability of DBN. In addition, the differencing transformation technique is utilized to ensure the stationarity of load time series for the application bagging and boosting methods. On the basis of the integrated thought of ensemble learning, a new hybrid ensemble algorithm is developed via integrating multiple separate ensemble methods. Considering the diversity in various ensemble algorithms, an effective K nearest neighbor classification method is utilized to adaptively determine the weights of sub-models. Furthermore, HEDL based probabilistic forecasting is proposed by taking advantage of the inherent resample idea in bagging and boosting. The effectiveness of the HEDL method for both deterministic and probabilistic forecasting has been systematically verified based on realistic load data from East China and Australia, indicating its promising prospective for practical applications in distribution networks. [ABSTRACT FROM AUTHOR]

An increasing number of residential customers have installed hybrid rooftop solar battery systems (HRSBSs). Currently, most HRSBSs are installed behind-the-meter (BTM), where only customers’ net load is measured by smart meters. This invisibility poses significant challenges to the system operation. Disaggregating BTM PV generation and battery charging/discharging profile of customers can enhance the grid-edge observability. This article proposes an energy disaggregation method to jointly separate the PV generation and battery charging/discharging power from the net load. First, a Home Smart Battery Management model is built to simulate the battery charging/discharging profile. Second, an optimal disaggregation model is established based on a contextually supervised source separation method. Furthermore, the feature vectors of PV, load, and battery are extracted as the input of the disaggregation model. Case studies on two datasets from Australia and China show that the proposed method has a promising disaggregation performance. [ABSTRACT FROM AUTHOR]

The current trend of increasing instant messaging (IM) use and its potential growth motivate this study. It offers a novel exploration of users' preferences for IM in the context of the use of other traditional and new communication media: face-to-face, telephone, email, and short messaging service (SMS) in two distinct cultures: Australia and China. It examines the impact of demographics, media experience, media richness perception, and national culture on media preferences. Our results, based on a student survey conducted in the two countries, show that women prefer IM for communication activities that require more attention and personal presence and prefer email for communication activities that require less personal presence; Communication technology experience may predict the adoption of new technology, such as IM and SMS, but has no effect on media that are already widely adopted, such as email. Email was clustered with face-to-face and telephone as the most preferred media for any communication activity, while IM and SMS clustered together and were the least preferred media for communication. After controlling for demographics and media experience, we found significant cultural differences in IM, telephone, and email preferences. Chinese preferred to use IM and telephone, while Australians preferred to use email. The cultural impact on technology use is persistent. [ABSTRACT FROM AUTHOR]