The BD4NRG Reference Architecture for Big Data Driven Energy Applications

Wehrmeister, Katharina Aurelia; Bothos, Efthimios; Marinakis, Vangelis; Magoutas, Babis; Pastor, Alexander; Carreras, Leonardo; Monti, Antonello

doi:10.1109/iisa56318.2022.9904424

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…BD4NRG architecture (Wehrmeister et al, 2022) has been designed on top of the BRIDGE architecture. Specifically, it consists of four layers illustrating the different layers of the data value chain and one vertical pillar that includes different dataspace enablers that are relevant with all the aforementioned layers.…”

Section: Related Workmentioning

confidence: 99%

"The I-Nergy Reference Architecture for the Provision of Next Generation Energy Services Through Artificial Intelligence"

2023

Proceedings of the International Conferences on E-Society 2023 and Mobile Learning 2023

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Recently the energy sector undergoes a rapid transformation that revolves around digitalization, decentralization and democratization. This is because global energy crisis contributes to rising poverty, raising prices and slowing economies. At the same time disrupting ICT technologies such as Artificial Intelligence (AI), Big Data Analytics, and Internet of Things (IoT) pose great potential towards improving several processes followed in the energy sector such as better forecasts for energy demand and consumption, that can contribute to significant energy savings. To this end, I-NERGY project aims at promoting and supporting AI in the energy sector through services and applications that support the entire energy value chain. In this publication, the I-NERGY big data reference architecture (RA) is presented, along with the most important technologies that have been used to cover the main requirements of I-NERGY platform. Moreover, I-NERGY is compared to other well-known big data RAs both general-purpose and energy sector specific, and the most important conclusions are presented in brief.

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Section: Related Workmentioning

confidence: 99%

"The I-Nergy Reference Architecture for the Provision of Next Generation Energy Services Through Artificial Intelligence"

2023

Proceedings of the International Conferences on E-Society 2023 and Mobile Learning 2023

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“…Additionally, the necessity for higher penetration of RES urges for more accurate forecasts that effectively adapt to the flexibility and demand response requirements posed within transmission and distribution operation systems. As a result, STLF has been investigated in a variety of research studies and projects [5,44,29,42,62].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers

Pelekis¹,

Seisopoulos²,

Spiliotis³

et al. 2023

Preprint

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Short-term load forecasting (STLF) is vital for the daily operation of power grids. However, the nonlinearity, non-stationarity, and randomness characterizing electricity demand time series renders STLF a challenging task. To that end, different forecasting methods have been proposed in the literature for day-ahead load forecasting, including a variety of deep learning models that are currently considered to achieve state-of-the-art performance. In order to compare the accuracy of such models, we focus on national net aggregated STLF and examine well-established autoregressive neural networks of indicative architectures, namely multi-layer perceptrons, N-BEATS, long short-term memory neural networks, and temporal convolutional networks, for the case of Portugal. To investigate the factors that affect the performance of each model and identify the most appropriate per case, we also conduct a post-hoc analysis, correlating forecast errors with key calendar and weather features. Our results indicate that N-BEATS consistently outperforms the rest of the examined deep learning models. Additionally, we find that external factors can significantly impact accuracy, affecting both the actual and relative performance of the models.

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“…Additionally, the necessity for higher penetration of renewable energy sources demands more accurate forecasts that effectively adapt to the flexibility and demand response requirements posed within transmission and distribution systems [5]. As a result, STLF has been investigated in a variety of recent research studies [6] and projects [7][8][9].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Transfer Learning for Day-Ahead Load Forecasting: A Case Study on European National Electricity Demand Time Series

Tzortzis,

Pelekis,

Spiliotis

et al. 2023

Mathematics

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Short-term load forecasting (STLF) is crucial for the daily operation of power grids. However, the non-linearity, non-stationarity, and randomness characterizing electricity demand time series renders STLF a challenging task. Various forecasting approaches have been proposed for improving STLF, including neural network (NN) models which are trained using data from multiple electricity demand series that may not necessarily include the target series. In the present study, we investigate the performance of a special case of STLF, namely transfer learning (TL), by considering a set of 27 time series that represent the national day-ahead electricity demand of indicative European countries. We employ a popular and easy-to-implement feed-forward NN model and perform a clustering analysis to identify similar patterns among the load series and enhance TL. In this context, two different TL approaches, with and without the clustering step, are compiled and compared against each other as well as a typical NN training setup. Our results demonstrate that TL can outperform the conventional approach, especially when clustering techniques are considered.

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The BD4NRG Reference Architecture for Big Data Driven Energy Applications

Cited by 6 publications

References 6 publications

"The I-Nergy Reference Architecture for the Provision of Next Generation Energy Services Through Artificial Intelligence"

"The I-Nergy Reference Architecture for the Provision of Next Generation Energy Services Through Artificial Intelligence"

A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers

Transfer Learning for Day-Ahead Load Forecasting: A Case Study on European National Electricity Demand Time Series

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