The penetration level of both photovoltaic and home automation systems is expected to increase in the short-term future in Germany and their combination will undoubtedly have some effect on the low-voltage grid. This study outlines the development of a high-resolution smart home power demand model taking into account the activity patterns of individuals, based on non-homogeneous Markov chain that are tuned to a German time use survey. The projected change in population size of Germany for the next years with the trends in photovoltaic, some automation system and efficient appliances, in combination with a home energy management algorithm are considered to estimate the future potential impacts of the increasing smart home incursion on the residential load profiles. The results show highly realistic patterns that capture annual and daily variations, load fluctuations and diversity between households as a function of number of persons. It is found that there is a 29.8% decrease in annual energy consumption when the home automation system acts to manage the power consumption of the devices for a current German household and a significant decrease of 70.1% for a future smart home scenario. Besides, the analysis undertaken in this study reveals that relative penetration of smart homes can cause an elevated variation in the daily demand profile up to 56% with respect to the current demand profile pattern.

In this paper, a development of ECG monitoring system for clinical and non-clinical environments is proposed. The prototype performs the tasks of general ECG monitoring, like detection and alarm generation for Tachycardia, bradycardia, acute myocardial infarction and ventricular fibrillation. It exposes features like real-time processing, long term data storage, data compression and Bluetooth communications with a mobile phone, resulting in an interesting and useful Home Care and Telemedicine application over GSM Network.