Since the Kyoto Protocol came into force in 2005, Europe is been pushed to turn itself into a green energy market. A lot of efforts had already been made, and a lot more is expected especially in the wind field. The plans of the wind industry to achieve the 20-20-20 program's targets include a 6 billion Euros investment to make offshore wind parks the most competitive energy source in Europe by 2030, when it's expected that this kind of energy will supply 33% of the EU's energy, and 50% by 2050. However, as only 1.5 GW of offshore WP was operating in 2008, there is still a lack of knowledge about the dynamic behavior of this kind of installations. The short distances involved in the collection grid, added to the multiple reflection points and possibility of constructive interference is suspected to be able to create overvoltages higher than the equipment's withstand levels.
Smart meters with automatic meter reading functionalities are becoming popular across the world. As a result, load measurements at various sampling frequencies are now available. Several methods have been proposed to infer device usage characteristics from household load measurements. However, many techniques are based on highly intensive computations that incur heavy computational costs; moreover, they often rely on private household information. In this paper, we propose a technique for the detection of appliance utilization patterns using low-computational-cost algorithms that do not require any information about households. Appliance utilization patterns are identified only from the system status behavior, represented by large system status datasets, by using dimensionality reduction and clustering algorithms. Principal component analysis, k-means, and the elbow method are used to define the clusters, and the minimum spanning tree is used to visualize the results that show the appearance of utilization patterns. Self organizing maps are used to create a system status classifier. We applied our techniques to two public datasets from two different countries, the United Kingdom (UK-DALE) and the US (REDD), with different usage patterns. The proposed clustering techniques enable effective demand-side management, while the system status classifier can detect appliance malfunctions only through system status analyses.
This paper focuses on the detection of utilization patterns in electricity residential consumption, which are closely related to the occupant characteristics (e.g. number, age, occupancy, and social class). Our goal is to identify groups of appliances that are often used together via their statistically relatedness. This relation might be obvious (as in TV and Home Theater), or not. The results can be used, for example, to guide a recommendations letter from the energy supplier to the final user, suggesting specific change of habits in order to improve the residence's energy efficiency. We propose here a methodology for identifying patterns from a large sets of system status, which is a computationally hard task defined in R n with n being the number of appliances involved. The approach consist in the following steps: (i) the Principal Component Analysis method is employed to reduce the set dimensionality to R 3 with explained variance from 68% to 90% to guarantee minimum information loses, (ii) the k-means method to clustering appliances into different groups and (iii) the elbow method was used to define the best number of clusters for each house with explained variance of at least 93% and reaching more than 99% for the best k. Numerical tests using the UK-DALE dataset are presented to show the effectiveness of the proposed solution. The main contribution of this work is a method with low computational cost that requires no other information than a large set of reliable system status (binary vectors) to reveal utilization patterns in the residence.
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