Strains and vectors for protein expression and secretion have been developed in the yeast Yarrowia lipolytica. Host strains were constructed with non-reverting auxotrophic markers, deletions of protease-encoding genes, and carrying a docking platform. To drive transcription, either the synthetic hp4d or the inducible POX2 promoter were used. Protein secretion is either directed by the targeting sequence of the alkaline extracellular protease or the extracellular lipase (LIP2p) signal sequence. We describe a set of vectors based on these promoters, targeting sequences and two URA3 alleles as selection markers. The wild-type URA3 allele, ura3d1, was used for single-copy integration and a mutant URA3 allele, ura3d4, was used to select for multi-copy integration into the genome. These vectors were used to express the Y. lipolytica extracellular lipase LIP2p and the Aspergillus oryzae leucine amino peptidase II. Lipase production under the control of the hp4d promoter by a strain containing a single copy reached 1000 U ml(-1) in shake flasks, while a strain containing multiple integrations reached 2000 U ml(-1) in shake flasks, 11500 U ml(-1) in batch and 90500 U ml(-1) in fed batch. Leucine amino peptidase production under the control of the hp4d promoter reached 320 mU ml(-1) in batch with a mono-copy lapA integrant and 28000 mU ml(-1) in fed batch with a multi-copy transformant.

Electricity load monitoring of appliances has become an important task considering the recent economic and ecological trends. In this game, machine learning has an important part to play, allowing for energy consumption understanding, critical equipment monitoring and even human activity recognition. This paper provides a survey of current researches on Intrusive Load Monitoring (ILM) techniques. ILM relies on lowend electricity meter devices spread inside the habitations, as opposed to Non-Intrusive Load Monitoring (NILM) that relies on an unique point of measurement, the smart meter. Potential applications and principles of ILMs are presented and compared to NILM. A focus is also given on feature extraction and machine learning algorithms typically used for ILM applications.

We report on the creation of a database of appliance consumption signatures and two test protocols to be used for appliance recognition tasks. By means of plug-based lowend sensors measuring the electrical consumption at low frequency, typically every 10 seconds, we made two acquisition sessions of one hour on about 100 home appliances divided into 10 categories: mobile phones (via chargers), coffee machines, computer stations (including monitor), fridges and freezers, Hi-Fi systems (CD players), lamp (CFL), laptops (via chargers), microwave oven, printers, and televisions (LCD or LED). We measured their consumption in terms of real power (W), reactive power (var), RMS current (A) and phase of voltage relative to current (ϕ). We now give free access to this ACS-F1 database. The proposed test protocols will help the scientific community to objectively compare new algorithms.

We present ACS-F2, a new electric consumption signature database acquired from domestic appliances. The scenario of use is appliance identification with emerging applications such as domestic electricity consumption understanding, load shedding management and indirect human activity monitoring. The novelty of our work is to use low-end electricity consumption sensors typically located at the plug. Our approach consists in acquiring signatures at a low frequency, which contrast with high frequency transient analysis approaches that are costlier and have been well studied in former research works. Electrical consumption signatures comprise real power, reactive power, RMS current, RMS voltage, frequency and phase of voltage relative to current. A total of 225 appliances were recorded over two sessions of one hour. The database is balanced with 15 different brands/models spread into 15 categories. Two realistic appliance recognition protocols are proposed and the database is made freely available to the scientific community for the experiment reproducibility. We also report on recognition results following these protocols and using baseline recognition algorithms like k-NN and GMM.