This paper discusses how usage patterns and preferences of inhabitants can be learned efficiently to allow smart homes to autonomously achieve energy savings. We propose a frequent sequential pattern mining algorithm suitable for real-life smart home event data. The performance of the proposed algorithm is compared to existing algorithms regarding completeness/correctness of the results, run times as well as memory consumption and elaborates on the shortcomings of the different solutions.We also propose a recommender system based on the developed algorithm. This recommender provides recommendations to the users to reduce their energy consumption. The recommender system was deployed to a set of test homes. The test participants rated the impact of the recommendations on their comfort. We used this feedback to adjust the system parameters and make it more accurate during a second test phase. The historical dataset provided by digitalSTROM contained 33 homes with 3521 devices and over 4 million events. The system produced 160 recommendations on the first phase and 120 on the second phase. The ratio of useful recommendations was close to 10%.

Sudden and violent disasters, such as earthquakes, represent significant challenges for health systems and services. This study shows the deficit of preparation and capacity to respond to a possible high-magnitude earthquake. The study also showed there are not enough resources to face mega-disasters, especially in large cities. Bambarén C , Uyen A , Rodriguez M . Estimation of the demand for hospital care after a possible high-magnitude earthquake in the City of Lima, Peru. Prehosp Disaster Med. 2017;32(1):106-111.

This paper presents a case study of a recommender system that can be used to save energy in smart homes without lowering the comfort of the inhabitants. We present an algorithm that mines consumer behavior data only and applies machine learning to suggest actions for inhabitants to reduce the energy consumption of their homes. The system looks for frequent and periodic patterns in the event data provided by the digitalSTROM home automation system. These patterns are converted into association rules, prioritized and compared with the current behavior of the inhabitants. If the system detects opportunities to save energy without decreasing the comfort level, it sends a recommendation to the inhabitants.The system was implemented and deployed to a set of test homes. The test participants were able to rate the impact of the recommendations on their comfort. This feedback was used to adjust the system parameters and make it more accurate during a second test phase. The historical data set provided by digitalSTROM contained 33 homes with 3521 devices and over 4 million events. The system produced 160 recommendations on the first phase and 120 on the second phase. The ratio of useful recommendations was close to 10%. We found out that a recommender system that uses an algorithm that mines patterns based on their confidence, independent of their frequency and periodicity, might achieve better results and a higher acceptance by users.

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Utility companies in developing countries employ analog electrical meters to determine consumption and bill their customers accordingly. Obtaining an accurate reading is an expensive and time-consuming process. High consumption levels of water, energy, or gas are fined by the government; thus, it is necessary to develop tools that allow users to be informed about their consumption in real time. This paper proposes a new number recognition algorithm named the Hausdorff distance for meter reading (HD MR). Experiments prove that HD MR can achieve a 99.9% recognition rate, even when recognized numbers are under rotation. The maximum recognition time is 31 ms; hence, the proposed method proves to be effective and capable in real time for the task proposed.