Insights into demand-side management with big data analytics in electricity consumers’ behaviour

Oprea, Simona-Vasilica; Bârã, Adela; Tudorica, Bogdan George; Calinoiu, Maria Irène; Botezatu, Mihai Alexandru

doi:10.1016/j.compeleceng.2020.106902

Cited by 39 publications

(19 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consumption data and questionnaires deployed by the Irish CER were involved in obtaining a clustering solution [23], the classification of load profiles [24,25], extracting insights from smart metering data and responses of electricity consumers [26], anomaly detection [27], and forecasts [28][29][30].…”

Section: Review Of the Literaturementioning

confidence: 99%

Smart Cities and Awareness of Sustainable Communities Related to Demand Response Programs: Data Processing with First-Order and Hierarchical Confirmatory Factor Analyses

et al. 2022

Self Cite

View full text Add to dashboard Cite

The mentality of electricity consumers is one of the most important entities that must be addressed when dealing with issues in the operation of power systems. Consumers are used to being completely passive, but recently these things have changed as significant progress of Information and Communication Technologies (ICT) and Internet of Things (IoT) has gained momentum. In this paper, we propose a statistical measurement model using a covariance structure, specifically a first-order confirmatory factor analysis (CFA) using SAS CALIS procedure to identify the factors that could contribute to the change of attitude within energy communities. Furthermore, this research identifies latent constructs and indicates which observed variables load on or measure them. For the simulation, two complex data sets of questionnaires created by the Irish Commission for Energy Regulation (CER) were analyzed, demonstrating the influence of some exogenous variables on the items of the questionnaires. The results revealed that there is a relevant relationship between the social–economic and the behavioral factors and the observed variables. Furthermore, the models provided a good fit to the data, as measured by the performance indicators.

show abstract

Section: Review Of the Literaturementioning

confidence: 99%

Smart Cities and Awareness of Sustainable Communities Related to Demand Response Programs: Data Processing with First-Order and Hierarchical Confirmatory Factor Analyses

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are also other incentives that can contribute to the achievement of the DR solutions. These non-price incentives can be based on pro-social attitudes [19,35].…”

Section: Price and Incentive Based Demand Response Solutionsmentioning

confidence: 99%

Key Factors Which Contribute to the Participation of Consumers in Demand Response Programs and Enable the Proliferation of Renewable Energy Sources

et al. 2021

View full text Add to dashboard Cite

Consumer behaviour in the energy field is playing a more important role in the new approach dominated by the proliferation of renewable energy sources. In this new context, the grid has to balance the intermittent and uncertain renewable energy generated, and find solutions, also, on the consumer side for increasing the stability and reliability of the energy system. The main de-mand response solutions are price and incentive based, but there is a need to identify the main factors which can influence their efficiency due to the fact that there is a lack of knowledge about the preferences of consumers. The main goal of this article is to identify the main demand response solutions and the related key factors which influence the participation of consumers in demand response programs and may contribute to the spread of renewable energy sources. For this research, semi-structured interviews were organised with experts in energy from Romania, Hungary and Serbia, as well as workshops with experts in energy, and an online survey with customers for electricity. This article reduces the gap between the results of technical studies, related in demand response programs, and their practical implementations, where the consumer behaviour and its social dimensions are neglected even though, in reality, they are playing the main role. The results suggest that the consumer’s participation in demand response programs is highly influenced by different aspects related to the promotion of the renewable energy and the reduction of CO2 emissions and the global warming impact.

show abstract

“…Reliable electricity load forecasting can offer great value to the above plans, and machine learning (ML) could revolutionize smart grids operations dealing with large amounts of data [6][7][8]. It is a field intensively researched because of the financial repercussions and the importance of optimally balancing the power grid [9].…”

Section: Context and Importance Of Load Forecastingmentioning

confidence: 99%

Analysis for Non-Residential Short-Term Load Forecasting Using Machine Learning and Statistical Methods with Financial Impact on the Power Market

2021

View full text Add to dashboard Cite

Short-term load forecasting predetermines how power systems operate because electricity production needs to sustain demand at all times and costs. Most load forecasts for the non-residential consumers are empirically done either by a customer’s employee or supplier personnel based on experience and historical data, which is frequently not consistent. Our objective is to develop viable and market-oriented machine learning models for short-term forecasting for non-residential consumers. Multiple algorithms were implemented and compared to identify the best model for a cluster of industrial and commercial consumers. The article concludes that the sliding window approach for supervised learning with recurrent neural networks can learn short and long-term dependencies in time series. The best method implemented for the 24 h forecast is a Gated Recurrent Unit (GRU) applied for aggregated loads over three months of testing data resulted in 5.28% MAPE and minimized the cost with 5326.17 € compared with the second-best method LSTM. We propose a new model to evaluate the gap between evaluation metrics and the financial impact of forecast errors in the power market environment. The model simulates bidding on the power market based on the 24 h forecast and using the Romanian day-ahead market and balancing prices through the testing dataset.

show abstract

Insights into demand-side management with big data analytics in electricity consumers’ behaviour

Cited by 39 publications

References 19 publications

Smart Cities and Awareness of Sustainable Communities Related to Demand Response Programs: Data Processing with First-Order and Hierarchical Confirmatory Factor Analyses

Smart Cities and Awareness of Sustainable Communities Related to Demand Response Programs: Data Processing with First-Order and Hierarchical Confirmatory Factor Analyses

Key Factors Which Contribute to the Participation of Consumers in Demand Response Programs and Enable the Proliferation of Renewable Energy Sources

Analysis for Non-Residential Short-Term Load Forecasting Using Machine Learning and Statistical Methods with Financial Impact on the Power Market

Contact Info

Product

Resources

About