Dataset Information

REEDD-CR: Residential electricity end-use demand dataset from Costa Rican households.

ABSTRACT: End-use demand data availability is a catalyst for improving energy efficiency measures and upgrading electricity demand studies. Nevertheless, residential end-use public datasets are limited, and end-use monitoring is costly. The lack of electricity end-use data is even more profound in Latin America, where there are no public end-use datasets as far as the authors are concerned. Hence, we present the Residential Electricity End-use Demand Dataset of Costa Rica (REEDD-CR), containing the results of monitoring 51 Costa Rican households. The data set includes the aggregated and branch circuit measurements for every home with a sample time of 1 min for at least an entire week. The measurements were distributed all around the country. In addition, based on these sub-measurements, REEDD-CR includes a dataset of 197 load signatures composed of seven consumption and demand features for eight high-consuming appliances: refrigerator, stove, dryer, lighting, water heating, air conditioning, microwave, and washing machine. The features included on each load signature are average power, peak power, average daily events, average daily energy, day-use factor, night-use factor, and time of use. The single-appliance measurements used to calculate these load signatures are also part of the dataset. The release of REEDD-CR can serve as a tool for appliance modeling, demand disaggregation testing, feedback for energy demand models, and the overall upgrade of electricity supply and demand simulation studies with realistic and disaggregated data.

SUBMITTER: Angulo-Paniagua J

PROVIDER: S-EPMC9800174 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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REEDD-CR: Residential electricity end-use demand dataset from Costa Rican households.

Angulo-Paniagua Jam J Victor-Gallardo Luis L Alfaro-Corrales Ignacio I Quirós-Tortós Jairo J

Data in brief 20221216

End-use demand data availability is a catalyst for improving energy efficiency measures and upgrading electricity demand studies. Nevertheless, residential end-use public datasets are limited, and end-use monitoring is costly. The lack of electricity end-use data is even more profound in Latin America, where there are no public end-use datasets as far as the authors are concerned. Hence, we present the Residential Electricity End-use Demand Dataset of Costa Rica (REEDD-CR), containing the result ...[more]

PMID: 36591381

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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

REEDD-CR: Residential electricity end-use demand dataset from Costa Rican households.

Publications

REEDD-CR: Residential electricity end-use demand dataset from Costa Rican households.

Similar Datasets

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