**1. Introduction**

Smart households are the future of new cities. The modernization of households involves the use of different Internet of Things (IoT) systems that allow monitoring and controlling the equipment installed in the households. These households generate a large amount of valuable data from the intelligent devices and appliances connected to an IoT system. The ability to use these data in real time makes it possible to analyse diverse information that has a significant impact on safety, the environment, and the economy of our society. Reports obtained from data in real time or stored over periods of time (days, weeks, months, and years) make it possible to study the behaviour of the household electricity demand.

Another consequence of this analysis is the adjustment of the term of power contracted with the supply company, which offers a significant reduction in the electricity bill. This leads to a more constant energy demand in the household. To achieve this feature, a priority system must be performed that in real time connects only those that do not exceed the contracted power limit, leaving on standby less priority equipment that would be connected when they are finished and have been assigned a higher priority.

To achieve these objectives, this study created a website with data from measurements obtained in different monitored households. In this sense, the main contributions provided in this paper are the following:


**Citation:** Cano-Ortega, A.; García-Cumbreras, M.A.; Sánchez-Sutil, F.; Hernández, J.C. A Platform for Analysing Huge Amounts of Data from Households, Photovoltaics, and Electrical Vehicles: From Data to Information. *Electronics* **2022**, *11*, 3991.

https://doi.org/10.3390/ electronics11233991

Academic Editor: Nikolay Hinov

Received: 17 October 2022 Accepted: 30 November 2022 Published: 1 December 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

developed, and sent to the cloud. The design of the system permits for the massive processing of multiple sets of household data, which enables studying the information obtained by applying different algorithms;


We designated a smart grid (SG) as a smart electricity distribution network. This is a two-way network capable of transmitting electricity in both directions, which allows households and different businesses to become small producers of electricity and not just consumers, as has been the case traditionally. Since SGs are combined with modern information technologies, they can provide data to both electricity distribution companies and consumers. One of the main tasks of SGs is the management and analysis of large amounts of generated information, known as big data.

The rest of the paper is organized as follows: Section 2 describes the related work; Section 3 shows the architecture of the system proposed; Sections 4 and 5 show the integration of the system and final screenshots, and finally our conclusions and future work are presented in Section 6.

#### **2. Related Work**

The study of information technologies (ICT) applied to Smart Cities and therefore, to smart grids and housing, is fundamental for the development of these paradigms.

IoT, cloud computing, and information analysis must use the optimal and highest speed ICT to achieve real-time data availability. In this respect Usman et al. [1] analysed the existing ICT adopted in SGs and their development over time. In this study, they analysed technologies such as Power Line Communication, Wireless Fidelity (Wi-Fi), Zigbee, Worldwide Interoperability for Microwave Access, Global System for Mobile General Packet Radio Service and DASH7.

#### *2.1. Smart Grids and Meter Data*

Numerous studies have integrated the use of SMs in SGs to monitor the behaviour of the different agents included within the network. Within this line of research, Munshi et al. [2] developed components based on big data for applications with SGs and the results obtained are transferred to a cloud computing platform. Kabalci [3] studied communication technologies and their security in data collection networks. Tanyali et al. [4] implemented a method for encrypting data taken by SMs since there is a risk of information theft when they are exposed on the network as well as finding out the user's habits. Khan et al. [5] dealt with the problem of integrating IoT sensors installed in households that have different communication protocols and technologies. To do this, they defined a standard in which all the sensors installed in the household were integrated (biometrics, security, electrical, etc.).

As for the cloud computing standards for SGs, Yigit et al. [6] defined the necessary architecture for cloud computing in SGs by using structures, methods, protocols, and algorithms. Al-Turjman et al. [7] reviewed the state-of-the-art in SGs, analysing the viability of using SGs to study and improve the quality and reliability of power. Al-Turjman et al. [8] studied the impact and efficiency of SMs, critical design factors, modifying and comparing parameters with real cases, and are categorized within this area. W. L. Rodrigues et al. [9] carried out a study on signal quality using a SM by means of cloud computing (fog computing). Recently, several studies have proposed systems and frameworks for the analysis of IoT data using various architectures related to cloud computing.

In this section, we discuss these studies, especially those that are representative of the state-of-the-art and similar to our work. It is important to note that a platform with cloud computing offers efficient resource processing of large IoT data in real time while providing information and processed data to the cloud for further processing and analysis. This integration design makes it easier for us to address cloud system latency issues that can have a significant impact on time-sensitive applications. The integration of SMs into IoT networks is another important part of the research related to SGs. Cano-Ortega et al. [10] developed equipment for power factor compensation using a TLBO optimization algorithm through a cloud data storage, control, and monitoring system. Cano-Ortega et al. [11] monitored the efficiency and the operating conditions of induction motors through an SM based on a LoRa LPWAN network. Sánchez-Sutil et al. [12] designed a measurement and control system for public lighting integrated in a LoRa LPWAN communication network. Asghari et al. [13] performed the current research techniques on IoT application approaches to analytically and statistically categorize this type of network. A. A. Mazhar Rathore, et al. [14] developed a combined system based on IoT for the development of Smart Cities using big data analysis. They used a complete system with several types of sensor deployment to make an SG. Naik et al. [15] designed an intelligent home management system based on IoT that uses sensors, actuators, smart phones, web services, and microcontrollers. This IoT platform and hardware are available through a mobile application. Pau et al. [16] made an intelligent metering infrastructure to automate and manage the distribution networks. The proposed architecture was based on a cloud solution, which allows communication with SMs and provides the necessary interfaces for the distribution of network services. Sánchez-Sutil et al. [17] developed and calibrated a low-cost SM to measure the electrical variables in homes supplied by photovoltaic solar energy. Moreover, in Ref. [18] the authors developed a smart plug to monitor and control electric load in a household with LoRaWAN network. Different web portals were analysed considering the electrical consumption measurements of households in different countries. As can be seen in Table 1, the measurements of the electrical variables have granularity varying between 1 s and 10 min; later, aggregations were made that can be used in different time horizons. Almost no time series below 1 s was used due to the large amount of data produced for each variable measured.

The websites shown in Table 1 store the recorded data and do not work in real time, the measurements of the electrical variables have a granularity that varies between 1 s and 10 min; the granularity times of the websites are less than1s[19–21], 1 s [19,20,22–27], between 1s and 1 min [19,22,28,29], and 10 min. Some allow downloading of the stored data for free and others for a fee. The websites do not display the data of all the monitored variables in real time. Only [22] can display data from the previous day, but this is paid. They do not allow comparisons between different households.

The SMs used in the websites are commercial devices where the measured data are sent every1s[20,22–25,27,28], every 1 min [29,30], and every 10 min. The websites do not indicate the costs associated with commercial SMs.


**Table 1.** Open access datasets of household power.

In the table, *v* is the voltage, *i* is the current, *PF* is the power factor, *p* is the active power, *q* is the reactive power, *s* is the apparent power, *e* is the energy and f is the frequency.

#### *2.2. Meter Data Analytics*

Data mining is the extraction of implicit information from other data. It can also be defined as the exploration and analysis, by automatic or semi-automatic means, of large amounts of data in order to discover meaningful patterns. Data mining techniques can be of two types: (i) descriptive, looking for interpretable patterns to describe data; (ii) predictive, using variables to predict future or unknown values of other variables.

The literature related to data mining, SGs, and SMs is varied. In this sense, Lui et al. [32], developed a big data system for data acquisition, processing, and analysis to create a database.

Other authors have studied big data applied to SGs [33] and performed a literature review on big data applied to electrical systems, defining the characteristics and future challenges. In addition, they analysed the characteristics of the SMs integrated into big data systems. Wilcox et al. [34], implemented a big data hardware/software system for the data analysis of household information stored in the cloud and with access to the data through a web portal. Yassine et al. [35] developed a big data analysis system based on an IoT network for measuring electricity consumption in households. Diamantoilakis et al. [36] applied big data-based methods for the real-time processing of data obtained by SMs. Tu et al. [37] proposed standards to be met by future big data systems applied to SGs.

In other investigations, the load profiles of households were analysed by applying big data-based techniques. Shyam et al. [38] studied data management techniques in the generation, transmission, distribution, and consumption of electrical energy. Saleh et al. [39] used measured data to obtain load predictions by applying filters for analysis. Guerrero et al. [40] developed a data mining algorithm to obtain an integrated database that reflects the consumption and load profiles of a household. Cano-Ortega et al. [41] developed a system for measuring electrical quantities to determine the load profiles of dwellings with a LoRa wireless network using an ABC optimization algorithm.

#### *2.3. Big Data Architecture and Cloud Computing*

Numerous studies have been carried out on SMs and big data. Lui et al. [32] applied a new development to ICT that allows reducing the data measured by SMs by utilizing analytical techniques. They developed a web portal and a scalable platform to process the measured data. Munshi et al. [2] implemented a platform of 6000 SMs with different data visualization and cloud computing scenarios. Yildiz et al. [42] performed methods for forecasting, clustering, classifying, and estimating the demand for electricity in households to optimize energy consumption. The paper by Funde et al. [43] was based on the unique combination of the symbolic aggregate approach (SAX), the discovery of temporal motifs, and the association mining rules to detect expected and unexpected patterns. The experimental data set obtained from the installed SMs supports the model developed in this research. Andreadou et al. [44] analysed parameters such as size, message transmission frequency, total transmission time, and buffer capacity and showed their effect on data obtained from medium voltage networks.

Meloni et al. [45] developed an architectural solution based on the Cloud-IoT for state estimation in SGs by combining cloud computing and the latest computer developments together with virtualization techniques for data processing. Razavi et al. [46] trained and developed genetic algorithms to predict the occupation status of households not only in the present but also in the future with a high degree of accuracy. Sial et al. [47] used heuristic techniques applied to data obtained with SMs to predict abnormal power consumption in campus residential buildings.

Araujo et al. [48] evaluated the performance of cloud storage systems. Yassine et al. [4] developed a platform for acquiring data from smart households using fog nodes and cloud computing to obtain the processing, analysis, and storage of the data measured. Forcan et al. [49] developed two communication models, cloud computing and fog nodes, to be used for estimating electricity losses in SGs and monitoring the voltage profile of a simulated IEEE system.
