Dataset Information

Electricity energy dataset "BanE-16": Analysis of peak energy demand with environmental variables for machine learning forecasting.

ABSTRACT: The "BanE-16" dataset is a comprehensive repository integrating electricity grid dynamics with meteorological variables for machine learning-based energy forecasting. Featuring peak energy demand, environmental factors (temperature, wind speed, atmospheric pressure), and electricity generation statistics, this dataset enables intricate analysis of weather-energy correlations. Its multidimensional nature facilitates predictive modeling, exploring intricate dependencies, and optimizing energy infrastructure. Leveraging machine learning methodologies, this dataset stands as a catalyst for innovative forecasting models and informed decision-making in energy management. Its diverse variables offer a holistic perspective, empowering researchers to delve into nuanced interrelationships, paving the way for sustainable energy planning and predictive analytics in dynamic energy ecosystems. Its multivariate nature empowers sophisticated machine-learning models, enabling precise energy forecasts and infrastructure optimizations. Researchers leveraging this dataset unlock the potential to delve deeper into intricate weather-energy relationships, driving advancements in predictive analytics for sustainable energy management. The integration of diverse variables lays the groundwork for innovative methodologies, steering the trajectory of informed decision-making in dynamic energy landscapes.

SUBMITTER: Salehin I

PROVIDER: S-EPMC10792676 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Electricity energy dataset "BanE-16": Analysis of peak energy demand with environmental variables for machine learning forecasting.

Salehin Imrus I Noman S M SM Hasan Mohammad Mahedy MM

Data in brief 20231218

The "BanE-16" dataset is a comprehensive repository integrating electricity grid dynamics with meteorological variables for machine learning-based energy forecasting. Featuring peak energy demand, environmental factors (temperature, wind speed, atmospheric pressure), and electricity generation statistics, this dataset enables intricate analysis of weather-energy correlations. Its multidimensional nature facilitates predictive modeling, exploring intricate dependencies, and optimizing energy infr ...[more]

PMID: 38235179

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Project description:Provision of network infrastructure to meet rising network peak demand is increasing the cost of electricity. Addressing this demand is a major imperative for Australian electricity agencies. The network peak demand model reported in this paper provides a quantified decision support tool and a means of understanding the key influences and impacts on network peak demand. An investigation of the system factors impacting residential consumers' peak demand for electricity was undertaken in Queensland, Australia. Technical factors, such as the customers' location, housing construction and appliances, were combined with social factors, such as household demographics, culture, trust and knowledge, and Change Management Options (CMOs) such as tariffs, price, managed supply, etc., in a conceptual 'map' of the system. A Bayesian network was used to quantify the model and provide insights into the major influential factors and their interactions. The model was also used to examine the reduction in network peak demand with different market-based and government interventions in various customer locations of interest and investigate the relative importance of instituting programs that build trust and knowledge through well designed customer-industry engagement activities. The Bayesian network was implemented via a spreadsheet with a tickbox interface. The model combined available data from industry-specific and public sources with relevant expert opinion. The results revealed that the most effective intervention strategies involve combining particular CMOs with associated education and engagement activities. The model demonstrated the importance of designing interventions that take into account the interactions of the various elements of the socio-technical system. The options that provided the greatest impact on peak demand were Off-Peak Tariffs and Managed Supply and increases in the price of electricity. The impact in peak demand reduction differed for each of the locations and highlighted that household numbers, demographics as well as the different climates were significant factors. It presented possible network peak demand reductions which would delay any upgrade of networks, resulting in savings for Queensland utilities and ultimately for households. The use of this systems approach using Bayesian networks to assist the management of peak demand in different modelled locations in Queensland provided insights about the most important elements in the system and the intervention strategies that could be tailored to the targeted customer segments.

Dataset Information

Electricity energy dataset "BanE-16": Analysis of peak energy demand with environmental variables for machine learning forecasting.

Publications

Electricity energy dataset "BanE-16": Analysis of peak energy demand with environmental variables for machine learning forecasting.

Similar Datasets

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