Prospects for energy economy modelling with big data: Hype, eliminating blind spots, or revolutionising the state of the art?

Li, Francis G.N.; Bataille, Chris; Pye, Steve; O’Sullivan, Aidan

doi:10.1016/j.apenergy.2019.02.002

Cited by 18 publications

(8 citation statements)

References 137 publications

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“…As energy storage and artificial intelligence improve the accessibility of energy sources, they will play an important role in energy production and consumption in the future. Furthermore, advances in machine-learning systems, such as artificial intelligence, will revolutionize the demand and supply of energy in our economy (Li et al, 2019;Jose et al, 2020). This is due to the capability of the smart grids and energy storage systems to effectively collect, analyze, and synthesize various data from a wide range of sources such as wind and solar, natural gas and coal to make decisions about the best allocation of energy resources.…”

Section: Literature Reviewmentioning

confidence: 99%

5G Wireless Networks in the Future Renewable Energy Systems

et al. 2021

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This paper focuses on the strategies that employ the fifth generation (5G) wireless networks in the optimal management of demand-side response in the future energy systems with the high penetration of renewable energy sources (RES). It also provides a comparison between advantages and challenges of 5G networks in demand-response renewable energy grids. Large-scale renewable energy integration always leads to a mismatch between generation and load demand in the short run due to the intermittency. It is often envisioned that 5G wireless networks that were recently launched and would most likely be fully deployed worldwide by 2035 would bring many technological and economic benefits for a plethora of the future high-renewables grids featuring electric transport and heating as well as prosumers generating renewable energy and trading it back to the grid (for example, in the vehicle-to-grid (V2G) framework) and among themselves using peer-to-peer (P2P) networks. Our paper offers a comprehensive analysis of 5G architecture with the perspectives of optimal management of demand-side response in the smart grids of the future. We show that the effective deployment of faster and more reliable wireless networks would allow faster data transfers and processing, including peer-to-peer (P2P) energy trade market, Internet of Vehicles (IoV) market, or faster smart metering, and thence open the path for the full-fledged Internet of Energy (IoE). Moreover, we show that 5G wireless networks might become in the future sustainable energy systems paving the road to even more advanced technologies and the new generations of networks. In addition, we demonstrate that for the effective management of energy demand-side response with a high share of renewables, certain forms of governments funding and incentives might be needed. These are required to strengthen the support of RES and helping to shift to the green economy.

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Section: Literature Reviewmentioning

confidence: 99%

5G Wireless Networks in the Future Renewable Energy Systems

et al. 2021

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“…Therefore, a hybrid approach using big data analysis has been selected. As Li et al (2019) suggests, the application of big data techniques has the potential to reduce blind spots in ESMs, such as the presented problem of characterising agriculture technologies.…”

Section: Quantitative Characterisation (Steps 4-6)mentioning

confidence: 99%

A novel energy systems model to explore the role of land use and reforestation in achieving carbon mitigation targets: A Brazil case study

Kerdan

Giarola

2019

Journal of Cleaner Production

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Due to its low global share of direct energy consumption and greenhouse gas emissions (1-2%), the implications of technological transitions in the agricultural and forestry sector on the energy system have been overlooked. This paper introduces the Agriculture and Land Use Sector module part of the ModUlar energy System Environment (MUSE), a novel energy system simulation model. The study presents a generalisable method that enables energy modellers to characterise agricultural technologies within an energy system modelling framework. Different mechanisation processes were characterised to simulate intensification/extensification transitions in the sector and its wider implications in the energy and land use system aiming at providing reliable non-energy outputs similarly to those found in dedicated land use models. Additionally, a forest growth model has been integrated to explore the role of reforestation alongside decarbonisation measures in the energy system in achieving carbon mitigation pathways. To illustrate the model's capabilities, Brazil is used as case study. Outputs suggest that by 2030 under a 2°C mitigation scenario, most of Brazil agricultural production would move from 'transitional' to 'modern' practices, improving productivity and reducing deforestation rates, at the expense of higher energy and fertiliser demand. By mid-century Brazil has the potential to liberate around 24.4 Mha of agricultural land, where large-scale reforestation could have the capacity to sequester around 5.6 GtCO 2 , alleviating mitigation efforts in the energy system, especially reducing carbon capture and storage technology investments in the industry and power sector.

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“…Current advancements and innovations in data mining, storage, processing, and retrieval have transformed science in various areas, including energy. These advancements will offer significant changes in energy models' operational environment [18]. Another widely used source of life in the world is natural gas as it is incredibly reliable and environment friendly.…”

Section: Introductionmentioning

confidence: 99%

Energy Consumption and Price Forecasting Through Data-Driven Analysis Methods: A Review

Patel

Shah

2021

SN COMPUT. SCI.

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Prediction of energy consumption and price is crucial in formatting policies related to the global energy market, demand, and supply. Data-driven analysis methods are giving rise to innovations in the world energy sector, including energy finance and economics. This paper has critically evaluated expand writings committed to Energy finance and economics applications of data-driven analysis. This paper comes up with an extensive view of state of the art in the area, which is already discussed with a different procedure. This review recognizes the applications of data-driven analysis methods in various areas such as forecasting domestic, nationwide, and transport energy consumption and price forecasting of multiple commodities, including crude oil, natural gas, and electricity. We have investigated certain research papers and given conclusion based on their researched and proposed model's prediction results and accuracies in respective areas. Our study suggests that Artificial Neural Network (ANN), Support Vector Machine (SVM), and other proposed neural network models are the most effective methods among other statistical and machine learning methods.

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Prospects for energy economy modelling with big data: Hype, eliminating blind spots, or revolutionising the state of the art?

Cited by 18 publications

References 137 publications

5G Wireless Networks in the Future Renewable Energy Systems

5G Wireless Networks in the Future Renewable Energy Systems

A novel energy systems model to explore the role of land use and reforestation in achieving carbon mitigation targets: A Brazil case study

Energy Consumption and Price Forecasting Through Data-Driven Analysis Methods: A Review

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