Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities

Abstract

New technologies such as sensor networks have been incorporated into the management of buildings for organizations and cities. Sensor networks have led to an exponential increase in the volume of data available in recent years, which can be used to extract consumption patterns for the purposes of energy and monetary savings. For this reason, new approaches and strategies are needed to analyze information in big data environments. This paper proposes a methodology to extract electric energy consumption patterns in big data time series, so that very valuable conclusions can be made for managers and governments. The methodology is based on the study of four clustering validity indices in their parallelized versions along with the application of a clustering technique. In particular, this work uses a voting system to choose an optimal number of clusters from the results of the indices, as well as the application of the distributed version of the k-means algorithm included in Apache Spark’s Machine Learning Library. The results, using electricity consumption for the years 2011–2017 for eight buildings of a public university, are presented and discussed. In addition, the performance of the proposed methodology is evaluated using synthetic big data, which cab represent thousands of buildings in a smart city. Finally, policies derived from the patterns discovered are proposed to optimize energy usage across the university campus.

Design and analysis of a novel power management approach, applied on a connected vehicle as V2V, V2B/I, and V2N

“…[35][36][37] The notion of connected vehicles can be beneficial to save energy inside the EVs, and several test and discussion in relation were exposed in the previous studies. 2,38,39 As the new commercialized vehicles were equipped with a wireless communication system, each vehicle can be proactive, cooperative, well-informed, and coordinated and will pave the way for supporting various applications for road safety. [40][41][42] Therefore, it is possible to guarantee, vehicle to building communication (V2B), or vehicle to road infrastructure communication (V2I) this characterizes the vehicle to the smart city connection.…”

“…Effectively, several types of researches proved that a smart city will encourage using the electrical transportation systems, as electric cars and electric buses, in which the problems related to the high recharge delay will be eliminated in the future . The notion of connected vehicles can be beneficial to save energy inside the EVs, and several test and discussion in relation were exposed in the previous studies …”

“…Actually, the production and the power transmission systems must be efficient for the sustainable development of the economy and environmental protection. 1,2 Saving energy is also a goal for consumer and producer. 3 In this context, a lot of researches were established.…”

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

“…Examining the papers selected and summarized in Table S15, presented in the Supplementary Materials file, it can be observed that 67% of them take into consideration smart buildings in general, while the remaining 33% refer to smart homes. The authors of these scientific articles made use of different types of sensors in their analyses, including binary sensors [26]; sensor networks [140]; smart meters, Personal Weather Stations (PWS), and sensors providing data useful in computing the mean values of: hourly indoor temperature, hourly outdoor temperature, hourly value of precipitation, hourly value of wind direction, hourly value of solar radiation, hourly value of ultraviolet index, hourly value of humidity, hourly value of pressure [42]. In these papers, the reasons for using the K-Means method with the sensor devices in smart buildings were related to extraction of behavioral patterns [26]; determining electricity consumption patterns [140]; and managing energy consumption [42].…”

Data Science and Big Data in Energy Forecasting

“…Unsupervised techniques have also been used to discover relevant patterns within consumption time series. In particular, data from a Spanish public university were analyzed in [5] in order to discover load profiles and reduce costs. Similar strategies were applied to determine whether Polish customers choose proper tariffs or not in [6].…”