Exploiting Scalable Machine-Learning Distributed Frameworks to Forecast Power Consumption of Buildings

Tania Cerquitelli, Giovanni Malnati and Daniele Apiletti
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Tania Cerquitelli: Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy
Giovanni Malnati: Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy
Daniele Apiletti: Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy

Energies, 2019, vol. 12, issue 15, 1-18

Abstract: The pervasive and increasing deployment of smart meters allows collecting a huge amount of fine-grained energy data in different urban scenarios. The analysis of such data is challenging and opening up a variety of interesting and new research issues across energy and computer science research areas. The key role of computer scientists is providing energy researchers and practitioners with cutting-edge and scalable analytics engines to effectively support their daily research activities, hence fostering and leveraging data-driven approaches. This paper presents SPEC, a scalable and distributed engine to predict building-specific power consumption. SPEC addresses the full analytic stack and exploits a data stream approach over sliding time windows to train a prediction model tailored to each building. The model allows us to predict the upcoming power consumption at a time instant in the near future. SPEC integrates different machine learning approaches, specifically ridge regression, artificial neural networks, and random forest regression, to predict fine-grained values of power consumption, and a classification model, the random forest classifier, to forecast a coarse consumption level. SPEC exploits state-of-the-art distributed computing frameworks to address the big data challenges in harvesting energy data: the current implementation runs on Apache Spark, the most widespread high-performance data-processing platform, and can natively scale to huge datasets. As a case study, SPEC has been tested on real data of an heating distribution network and power consumption data collected in a major Italian city. Experimental results demonstrate the effectiveness of SPEC to forecast both fine-grained values and coarse levels of power consumption of buildings.

Keywords: big data frameworks; data mining algorithms; machine learning; energy consumption forecast; data streams analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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