introduction of a new health-care delivery paradigm [3]. Besides, low-cost sensing solutions, whose wireless services coupled with rapid advances in data analysis, have provided the next generation of products to be deployed within living environments. These have the potential to improve the manner where remote health-care support can be provided and are slowly gaining increased acceptance by both users and health-care professionals [4]. From the multitude of health scenarios to consider, detecting falls within the living environment is a relevant challenge with a high impact in terms of both security and safety. Accidental falls can cause serious injury to at-risk individuals, especially for the aging [5]. Within this cohort, falls are the leading cause of hospitalization, injury-related deaths and loss of independence. However, it has been demonstrated that detecting and rapidly responding to falls can reduce the long-term risks associated with falls. Although efforts have been directed towards supporting the detection and management of falls within living environments, a range of issues still exist. From a usability perspective, challenges are faced by the costs of the solution and the perceived issue of intrusiveness when video based cameras are used. From a technical perspective, challenges are faced by levels of accuracy levels and a desire to reduce the numbers of false positives given the implications that these have from a health-care provision perspective. In addition, the studies of fall detection are mainly

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With the increasing penetration of wind power in renewable energy systems, it is important to improve the accuracy of wind speed prediction. However, wind power generation has great uncertainties which make high-quality interval prediction a challenge. Existing multi-objective optimization interval prediction methods do not consider the robustness of the model. Thus, trained models for wind speed interval prediction may not be optimal for future predictions. In this paper, the prediction interval coverage probability, the prediction interval average width, and the robustness of the model are used as three objective functions for determining the optimal model of short-term wind speed interval prediction using multi-objective optimization. Furthermore, a new Stochastic Sensitivity for Prediction Intervals (SS_PIs) is proposed in this work to measure the stability and robustness of the model for interval prediction. Using wind farm data from countries on two different continents as case studies, experimental results show that the proposed method yields better prediction intervals in terms of all metrics including prediction interval coverage probability (PICP), prediction interval normalized average width (PINAW) and SS_PIs. For example, at the prediction interval nominal confidence (PINC) of 85%, 90% and 95%, the proposed method has the best performance in all metrics of the USA wind farm dataset.

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