A significant amount of energy is wasted by electrical appliances when they operate inefficiently either due to anomalies and/or incorrect usage. To address this problem, we present SocketWatch -an autonomous appliance monitoring system. SocketWatch is positioned between a wall socket and an appliance. SocketWatch learns the behavioral model of the appliance by analyzing its active and reactive power consumption patterns. It detects appliance malfunctions by observing any marked deviations from these patterns.SocketWatch is inexpensive and is easy to use: it neither requires any enhancement to the appliances nor to the power sockets nor any communication infrastructure. Moreover, the decentralized approach avoids communication latency and costs, and preserves data privacy. Real world experiments with multiple appliances indicate that SocketWatch can be an effective and inexpensive solution for reducing electricity wastage.

In social media, some people use positive words to express negative opinion on a topic which is known as sarcasm. The existence of sarcasm becomes special because it is hard to be detected using simple sentiment analysis technique. Research on sarcasm detection in Indonesia is still very limited. Therefore, this research proposes a technique in detecting sarcasm in Indonesian Twitter feeds particularly on several critical issues such as politics, public figure and tourism. Our proposed technique uses two feature extraction methods namely interjection and punctuation. These methods are later used in two different weighting and classification algorithms. The empirical results demonstrate that combination of feature extraction methods, tf-idf, k-Nearest Neighbor yields the best performance in detecting sarcasm.

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In this paper we describe an ongoing project which develops an automated residential Demand Response (DR) system that attempts to manage residential loads in accordance with DR signals. In this early stage of the project, we propose an approach for identifying individual appliance consumption from the aggregate load and discuss the effectiveness of load disaggregation techniques when total load data also includes appliances that are unmonitored even during the training phase. We show that simple discriminative methods can directly predict the appliance states (e.g. on, off, standby) and the predicted state can be used to calculate energy consumed by the appliances. We also show that these methods perform substantially better than the generative models of energy consumption that are commonly used. We evaluated the proposed approach using publicly available REDD data set, and our experimental evaluation demonstrates the improvement in accuracy.

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