Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study

Miriam Benedetti, Vittorio Cesarotti, Vito Introna and Jacopo Serranti

Applied Energy, 2016, vol. 165, issue C, 60-71

Abstract: Energy consumption control in energy intensive companies is always more considered as a critical activity to continuously improve energy performance. It undoubtedly requires a huge effort in data gathering and analysis, and the amount of these data together with the scarceness of human resources devoted to Energy Management activities who could maintain and update the analyses’ output are often the main barriers to its diffusion in companies. Advanced tools such as software based on machine learning techniques are therefore the key to overcome these barriers and allow an easy but accurate control. This type of systems is able to solve complex problems obtaining reliable results over time, but not to understand when the reliability of the results is declining (a common situation considering energy using systems, often undergoing structural changes) and to automatically adapt itself using a limited amount of training data, so that a completely automatic application is not yet available and the automatic energy consumption control using intelligent systems is still a challenge.

Keywords: Energy model; Energy management; Artificial Neural Networks; Adaptive algorithms (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (39)

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DOI: 10.1016/j.apenergy.2015.12.066

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