Home energy management systems can be used to monitor and optimize consumption and local production from renewable energy. To assess solutions before their deployment, researchers and designers of those systems demand for energy consumption datasets. In this paper, we present the GREEND dataset, containing detailed power usage information obtained through a measurement campaign in households in Austria and Italy. We provide a description of consumption scenarios and discuss design choices for the sensing infrastructure. Finally, we benchmark the dataset with state-of-the-art techniques in load disaggregation, occupancy detection and appliance usage mining.

Households account for a significant fraction of overall energy consumption. Energy usage can be reduced by improving the efficiency of devices and optimizing their use as well as by encouraging people to change their behaviour towards a more sustainable lifestyle. In this study, we investigate patterns of domestic energy use in Carinthia (Austria) and Friuli-Venezia Giulia (Italy). In particular, we report the results of an online survey about electrical devices and their use in households. We outline typical scenarios in the two regions and discuss possible strategies to reduce the consumption of energy in these regions.

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Demand response provides utilities with a mechanism to share with end users the stochasticity resulting from the use of renewable sources. Pricing is accordingly used to reflect energy availability, to allocate such a limited resource to those loads that value it most. However, the strictly competitive mechanism can result in service interruption in presence of competing demand. To solve this issue we investigate on the use of forward contracts, i.e., service-level agreements priced to reflect the expectation of future supply and demand curves. Given the limited resources of microgrids, service interruption is an opposite objective to the one of service availability. We firstly design policy-based brokers and identify then a learning broker based on artificial neural networks. We show the latter being progressively minimizing the reimbursement costs and maximizing the overall profit.