Gaining insights into dwelling characteristics using machine learning for policy making on nearly zero-energy buildings with the use of smart meter and weather data

Čurčić, Teo; Kalloe, Rajeev R.; Kreszner, Merel A.; Luijk, Olivier van; Puchol, Santiago Puertas; Batuecas, Emilio Caba; Rahola, T.B. Salcedo

doi:10.13044/j.sdewes.d9.0388

Cited by 9 publications

(1 citation statement)

References 13 publications

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“…The regression version, known as support vector regression (SVR) [57], creates a region in hyperspace where the data points are found. Recent examples of SVR are present in investigating the penetration of photovoltaic sources in microgrids [58] and the classification of household characteristics [59].…”

Section: Energy Consumption Forecasting Methodsmentioning

confidence: 99%

Using Industry 4.0’s Big Data and IoT to Perform Feature-Based and Past Data-Based Energy Consumption Predictions

et al. 2022

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Industry 4.0 and its technologies allow advancements in communications, production and management efficiency across several segments. In smart grids, essential parts of smart cities, smart meters act as IoT devices that can gather data and help the management of the sustainable energy matrix, a challenge that is faced worldwide. This work aims to use smart meter data and household features data to seek the most appropriate methods of energy consumption prediction. Using the Cross-Industry Standard Process for Data Mining (CRISP-DM) method, Python Platform, and several prediction methods, prediction experiments were performed with household feature data and past consumption data of over 470 smart meters that gathered data for three years. Support vector machines, random forest regression, and neural networks were the best prediction methods among the ones tested in the sample. The results help utilities (companies that maintain the infrastructure for public services) to offer better contracts to new households and to manage their smart grid infrastructure based on the forecasted demand.

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Section: Energy Consumption Forecasting Methodsmentioning

confidence: 99%