Abstract
The smart metered electricity consumption data and high dimensional questionnaires provide useful information for designing the tariffs aimed at reducing electricity consumption and peak. The volume of data generated by smart meters for a sample of around four thousand residential consumers requires Not only Structured Query Language (NoSQL) solutions, data management and artificial neural network clustering algorithms, such as Self-Organizing Maps. In this paper, we propose a novel methodology that handles a large volume of data and extracts information from electricity consumption measured at 30 min and from complex questionnaires. Five three-level Time-of-Use tariffs are altered and investigated to minimize the consumers’ payment. Then, input data analysis revealed that the peak consumption is influenced by a segment of consumers that can be targeted to flatten the peak. Based on simulations, more than 23% of the peak consumption can be reduced by shifting it from peak to off-peak hours.
Highlights
From vertically integrated utility companies to the distributed energy sources, this industry has transformed tremendously, facing new challenges that come along with the Information Communication Technology (ICT) progress
The electricity flows in both directions, from and to the grid, and it is frequently measured by Smart Meters (SM) that generate a large volume of data
The electricity consumers are subject to complex questionnaires that can be deployed by regulators, grid operators or suppliers, to better understand and predict consumers behavior and trends
Summary
From vertically integrated utility companies to the distributed energy sources, this industry has transformed tremendously, facing new challenges that come along with the Information Communication Technology (ICT) progress. The energy generated in remote large power plants has been replaced by new distributed generation sources located close to residential areas, which modified the unidirectional flows direction. The electricity consumers are subject to complex questionnaires (pre- and post-trial) that can be deployed by regulators, grid operators or suppliers, to better understand and predict consumers behavior and trends. Their answers are useful in designing the demand response strategies, including advanced Time-of-Use (ToU) tariffs [2], implemented thanks to communications progress and SM that can incentive consumers to energize their appliances at lower tariffs. The consumption, appliances data and consumer answers (opinions) are significant data sources, pouring from high-dimensional surveys and SM, which will be analyzed in this paper
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