IEEE Access (Jan 2022)
eLAMI—An Innovative Simulated Dataset of Electrical Loads for Advanced Smart Energy Applications
Abstract
Smart Energy Applications are particularly impacting, especially due to energy resource scarcity and its high associated costs. Smart management of energy consumption derives both from the user lifestyle, in terms of efficient and responsible behaviors, and from automatic algorithms that control and counteract energy waste and inefficient management. Focusing the attention on the latter, the development of methodologies and well-working techniques to monitor and optimize consumption often requires an important effort in long measurement campaigns to get raw data to work with. Whenever this should be too much expensive or proper instrumentation is unavailable, public datasets could solve the problem. The current literature review on the dataset availability showed a large presence of information, especially related to electrical energy consumption. Nevertheless, several limitations affect them, from the low number of calculated electrical parameters (i.e. 4-5 in most cases) to short analysis periods, passing by the lack of detailed frequency domain information or poor consumption habit transitions analysis. Accordingly, this work aims to overcome current dataset limitations, by proposing a real-measurement based simulated dataset, extracting more than 400 discriminative electrical parameters on 36 different home appliances, discussing preliminary acquisition set-ups, simulation process, extracted electrical parameters and examples of applicability to smart energy applications. To provide a data quality index, a validation procedure has also been carried out, showing how simulated data match real acquisition with a reference measurement instrument. The produced dataset is available for downloading and analysis in public free access and its repository link is provided in the reference section.
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