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The Big Data Value Chain for the Provision of AI-Enabled Energy Analytics Services

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Machine Learning Applications for Intelligent Energy Management

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 35))

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Abstract

In order to support decision-making problems on the energy sector, like energy forecasting and demand prediction, analytics services are developed that assist users in extracting useful inferences on energy related data. Such analytics services use AI techniques to extract useful knowledge on collected data from energy infrastructure like smart meters and sensors. The big data value chain describes the steps of big data life cycle from collecting, pre-processing, storing and querying energy consumption data for high-level user-driven services. With the exponential growth of networking capabilities and the Internet of Things (IoT), data from the energy sector is arriving with a high throughput taking the problem of calculating big data analytics to a new level. This research will review existing approaches for big data energy analytics services and will further propose a framework for facilitating AI-enabled energy analytics taking into consideration all the requirements of analytics services through the entire big data value chain from data acquisition and batch and stream data ingestion, to creating proper querying mechanisms. Those query mechanisms will in turn enable the execution of queries on huge volumes of energy consumption data with low latency, and establishing high-level data visualizations. The proposed framework will also address privacy and security concerns regarding the big data value chain and allow easy applicability and adjustment on various use cases on energy analytics.

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Abbreviations

Term:

Abbreviation

Internet of things:

IoT

Machine learning:

ML

Neural network:

NN

Support vector machine:

SVM

Online analytical processing:

OLAP

Deep learning:

DL

Online transactional processing:

OLTP

Atomicity, consistency, isolation and durability:

ACID

Hadoop distributed file system:

HDFS

International dataspaces:

IDS

Artificial neural network:

ANN

Multiple linear regression:

MLR

Multiple layer perceptron:

MLP

Building management system:

BMS

Building information management:

BIM

Infrastructure as a service:

IaaS

Platform as a service:

PaaS

Software as a service:

SaaS

Identity access management:

IAM

Generic enabler:

GE

Context broker:

CB

Machine to machine:

M2M

Linear model:

LM

K-nearest neighbour:

KNN

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Authors and Affiliations

  1. Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str, 15773, Athens, Greece

    Konstantinos Touloumis, Evangelos Karakolis, Panagiotis Kapsalis, Sotiris Pelekis & Dimitris Askounis

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  1. Konstantinos Touloumis
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  2. Evangelos Karakolis
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  3. Panagiotis Kapsalis
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  4. Sotiris Pelekis
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  5. Dimitris Askounis
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Correspondence to Evangelos Karakolis .

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Editors and Affiliations

  1. Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

    Haris Doukas

  2. Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

    Vangelis Marinakis

  3. Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

    Elissaios Sarmas

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Touloumis, K., Karakolis, E., Kapsalis, P., Pelekis, S., Askounis, D. (2024). The Big Data Value Chain for the Provision of AI-Enabled Energy Analytics Services. In: Doukas, H., Marinakis, V., Sarmas, E. (eds) Machine Learning Applications for Intelligent Energy Management. Learning and Analytics in Intelligent Systems, vol 35. Springer, Cham. https://doi.org/10.1007/978-3-031-47909-0_2

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