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17 pages, 10993 KiB

Open AccessCommunication

A Customized Energy Management System for Distributed PV, Energy Storage Units, and Charging Stations on Kinmen Island of Taiwan

by Hsi-Chieh Lee, Hua-Yueh Liu, Tsung-Chieh Lin and Chih-Ying Lee

Sensors 2023, 23(11), 5286; https://doi.org/10.3390/s23115286 - 02 Jun 2023

Cited by 2 | Viewed by 1440

Abstract

Kinmen, the famous Cold War island also known as Quemoy, is a typical island with isolated power grids. It considers the promotion of renewable energy and electric charging vehicles to be two essential strategies to achieve the goal of a low-carbon island and [...] Read more.

Kinmen, the famous Cold War island also known as Quemoy, is a typical island with isolated power grids. It considers the promotion of renewable energy and electric charging vehicles to be two essential strategies to achieve the goal of a low-carbon island and smart grid. With this motivation in mind, the main objective of this study is to design and deploy an energy management system for hundreds of current PV sites distributed on the island, energy storage systems, and charging stations on the island. In addition, the real-time acquisition of the data for power generation, power storage, and power consumption systems will be used for future demand and response analysis. Moreover, the accumulated dataset will also be utilized for the forecast or prediction of renewable energy generated by the PV systems or power consumed by the battery units or charging stations. The results of this study are promising since a practical, robust, and workable system and database are developed and implemented with a variety of Internet of Things (IoT), data transmission technologies, and the hybrid of on-premises and cloud servers. Users of the proposed system can remotely access the visualized data through the user-friendly web-based and Line bot interfaces seamlessly. Full article

(This article belongs to the Special Issue Smart IoT System for Renewable Energy Resource)

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18 pages, 5709 KiB

Open AccessArticle

Design and Implementation of a Cloud-IoT-Based Home Energy Management System

by Felipe Condon, José M. Martínez, Ali M. Eltamaly, Young-Chon Kim and Mohamed A. Ahmed

Sensors 2023, 23(1), 176; https://doi.org/10.3390/s23010176 - 24 Dec 2022

Cited by 9 | Viewed by 5753

Abstract

The advances in the Internet of Things (IoT) and cloud computing opened new opportunities for developing various smart grid applications and services. The rapidly increasing adoption of IoT devices has enabled the development of applications and solutions to manage energy consumption efficiently. This [...] Read more.

The advances in the Internet of Things (IoT) and cloud computing opened new opportunities for developing various smart grid applications and services. The rapidly increasing adoption of IoT devices has enabled the development of applications and solutions to manage energy consumption efficiently. This work presents the design and implementation of a home energy management system (HEMS), which allows collecting and storing energy consumption data from appliances and the main load of the home. Two scenarios are designed and implemented: a local HEMS isolated from the Internet and relies on its processing and storage duties using an edge device and a Cloud HEMS using AWS IoT Core to manage incoming data messages and provide data-driven services and applications. A testbed was carried out in a real house in the city of Valparaiso, Chile, over a one-year period, where four appliances were used to collect energy consumption using smart plugs, as well as collecting the main energy load of the house through a data logger acting as a smart meter. To the best of our knowledge, this is the first electrical energy dataset with a 10-second sampling rate from a real household in Valparaiso, Chile. Results show that both implementations perform the baseline tasks (collecting, storing, and controlling) for a HEMS. This work contributes by providing a detailed technical implementation of HEMS that enables researchers and engineers to develop and implement HEMS solutions to support different smart home applications. Full article

(This article belongs to the Special Issue Smart IoT System for Renewable Energy Resource)

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20 pages, 3330 KiB

Open AccessArticle

Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data

by Purna Prakash Kasaraneni, Yellapragada Venkata Pavan Kumar, Ganesh Lakshmana Kumar Moganti and Ramani Kannan

Sensors 2022, 22(23), 9323; https://doi.org/10.3390/s22239323 - 30 Nov 2022

Cited by 11 | Viewed by 1635

Abstract

Addressing data anomalies (e.g., garbage data, outliers, redundant data, and missing data) plays a vital role in performing accurate analytics (billing, forecasting, load profiling, etc.) on smart homes’ energy consumption data. From the literature, it has been identified that the data imputation with [...] Read more.

Addressing data anomalies (e.g., garbage data, outliers, redundant data, and missing data) plays a vital role in performing accurate analytics (billing, forecasting, load profiling, etc.) on smart homes’ energy consumption data. From the literature, it has been identified that the data imputation with machine learning (ML)-based single-classifier approaches are used to address data quality issues. However, these approaches are not effective to address the hidden issues of smart home energy consumption data due to the presence of a variety of anomalies. Hence, this paper proposes ML-based ensemble classifiers using random forest (RF), support vector machine (SVM), decision tree (DT), naive Bayes, K-nearest neighbor, and neural networks to handle all the possible anomalies in smart home energy consumption data. The proposed approach initially identifies all anomalies and removes them, and then imputes this removed/missing information. The entire implementation consists of four parts. Part 1 presents anomaly detection and removal, part 2 presents data imputation, part 3 presents single-classifier approaches, and part 4 presents ensemble classifiers approaches. To assess the classifiers’ performance, various metrics, namely, accuracy, precision, recall/sensitivity, specificity, and F1 score are computed. From these metrics, it is identified that the ensemble classifier “RF+SVM+DT” has shown superior performance over the conventional single classifiers as well the other ensemble classifiers for anomaly handling. Full article

(This article belongs to the Special Issue Smart IoT System for Renewable Energy Resource)

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23 pages, 6496 KiB

Open AccessArticle

Toward an Intelligent Campus: IoT Platform for Remote Monitoring and Control of Smart Buildings

by Mohamed A. Ahmed, Sebastian A. Chavez, Ali M. Eltamaly, Hugo O. Garces, Alejandro J. Rojas and Young-Chon Kim

Sensors 2022, 22(23), 9045; https://doi.org/10.3390/s22239045 - 22 Nov 2022

Cited by 6 | Viewed by 2933

Abstract

With the growing need to obtain information about power consumption in buildings, it is necessary to investigate how to collect, store, and visualize such information using low-cost solutions. Currently, the available building management solutions are expensive and challenging to support small and medium-sized [...] Read more.

With the growing need to obtain information about power consumption in buildings, it is necessary to investigate how to collect, store, and visualize such information using low-cost solutions. Currently, the available building management solutions are expensive and challenging to support small and medium-sized buildings. Unfortunately, not all buildings are intelligent, making it difficult to obtain such data from energy measurement devices and appliances or access such information. The internet of things (IoT) opens new opportunities to support real-time monitoring and control to achieve future smart buildings. This work proposes an IoT platform for remote monitoring and control of smart buildings, which consists of four-layer architecture: power layer, data acquisition layer, communication network layer, and application layer. The proposed platform allows data collection for energy consumption, data storage, and visualization. Various sensor nodes and measurement devices are considered to collect information on energy use from different building spaces. The proposed solution has been designed, implemented, and tested on a university campus considering three scenarios: an office, a classroom, and a laboratory. This work provides a guideline for future implementation of intelligent buildings using low-cost open-source solutions to enable building automation, minimize power consumption costs, and guarantee end-user comfort. Full article

(This article belongs to the Special Issue Smart IoT System for Renewable Energy Resource)

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24 pages, 7135 KiB

Open AccessArticle

Design and Development of an IoT Smart Meter with Load Control for Home Energy Management Systems

by Omar Munoz, Adolfo Ruelas, Pedro Rosales, Alexis Acuña, Alejandro Suastegui and Fernando Lara

Sensors 2022, 22(19), 7536; https://doi.org/10.3390/s22197536 - 05 Oct 2022

Cited by 7 | Viewed by 8399

Abstract

Electricity consumption is rising due to population growth, climate change, urbanization, and the increasing use of electronic devices. The trend of the Internet of Things has contributed to the creation of devices that promote the thrift and efficient use of electrical energy. Currently, [...] Read more.

Electricity consumption is rising due to population growth, climate change, urbanization, and the increasing use of electronic devices. The trend of the Internet of Things has contributed to the creation of devices that promote the thrift and efficient use of electrical energy. Currently, most projects relating to this issue focus solely on monitoring energy consumption without providing relevant parameters or switching on/off electronic devices. Therefore, this paper presents in detail the design, construction, and validation of a smart meter with load control aimed at being part of a home energy management system. With its own electronic design, the proposal differs from others in many aspects. For example, it was developed using a simple IoT architecture with in-built WiFi technology to enable direct connection to the internet, while at the same time being big enough to be part of standardized electrical enclosures. Unlike other smart meters with load control, this one not only provides the amount of energy consumption, but rms current and voltage, active, reactive, and apparent power, reactive energy, and power factor—parameters that could be useful for future studies. In addition, this work presents evidence based on experimentation that the prototype in all its readings achieves an absolute percentage error of less than 1%. A real-life application of the device was also demonstrated in this document by measuring different appliances and switching them on/off manually and automatically using a web-deployed application. Full article

(This article belongs to the Special Issue Smart IoT System for Renewable Energy Resource)

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11 pages, 3116 KiB

Open AccessArticle

High-Performance Breaking and Intelligent of Miniature Circuit Breakers

by Jianning Yin, Xiaojian Lang, Haotian Xu and Jiandong Duan

Sensors 2022, 22(16), 5990; https://doi.org/10.3390/s22165990 - 11 Aug 2022

Cited by 3 | Viewed by 2916

Abstract

The exploitation and utilization of clean energy such as wind and photovoltaic power plays an important role in the reduction in carbon emissions to achieve the goal of “emission peak and carbon neutral”, but such a quantity of clean energy accessing the electric [...] Read more.

The exploitation and utilization of clean energy such as wind and photovoltaic power plays an important role in the reduction in carbon emissions to achieve the goal of “emission peak and carbon neutral”, but such a quantity of clean energy accessing the electric system will foster the transition of the electric power system structure. The intelligentization of power equipment will be an inevitable trend of development. High breaking performance, remote control and a digital detection platform of miniature circuit breaker, a protective equipment of a power distribution system, have also been inevitable requirements of the power Iot system. Based on the above, this paper studies three aspects: high-performance AC and DC general switching technology, remote control technology and operation status’ digital monitoring. A new DC non-polar breaking technology is proposed, which improves the short circuit breaking ability. An experimental prototype using the above techniques was fabricated and passed the DC 1000 V/10 kA short-circuit breaking test. On the basis of the above, an intelligent circuit breaker is developed, which contains multiple functions: remote switching, real-time temperature detection, energy metering and fault warning. Moreover, a software for digital condition monitoring and remote control is developed. This work has certain theoretical and practical significance for the development of the power Internet of things. Full article

(This article belongs to the Special Issue Smart IoT System for Renewable Energy Resource)

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35 pages, 20051 KiB

Open AccessArticle

Smart Control and Energy Efficiency in Irrigation Systems Using LoRaWAN

by Francisco Sánchez-Sutil and Antonio Cano-Ortega

Sensors 2021, 21(21), 7041; https://doi.org/10.3390/s21217041 - 24 Oct 2021

Cited by 16 | Viewed by 3569

Abstract

Irrigation installations in cities or agricultural operations use large amounts of water and electrical energy in their activity. Therefore, optimising these resources is essential nowadays. Wireless networks offer ideal support for such applications. The long-range wide-area network (LoRaWAN) used in this research offers [...] Read more.

Irrigation installations in cities or agricultural operations use large amounts of water and electrical energy in their activity. Therefore, optimising these resources is essential nowadays. Wireless networks offer ideal support for such applications. The long-range wide-area network (LoRaWAN) used in this research offers a large coverage of up to 5 km, has low power consumption and does not need additional hardware such as repeaters or signal amplifiers. This research develops a control and monitoring system for irrigation systems. For this purpose, an irrigation algorithm is designed that uses rainfall probability data to regulate the irrigation of the installation. The algorithm is complemented by checking the sending and receiving of information in the LoRa network to reduce the loss of information packets. In addition, two temperature and humidity measurement devices for LoRaWAN (THMDLs) and an electrovalve control device for LoRaWAN (ECDLs) were developed. The hardware and software were also designed, and prototypes were built with the development of the electronic board. The wide coverage of the LoRaWAN allows the covering of small to large irrigation areas. Full article

(This article belongs to the Special Issue Smart IoT System for Renewable Energy Resource)

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32 pages, 2801 KiB

Open AccessArticle

A New Hybrid Algorithm for Multi-Objective Reactive Power Planning via FACTS Devices and Renewable Wind Resources

by Rahmad Syah, Peyman Khorshidian Mianaei, Marischa Elveny, Naeim Ahmadian, Dadan Ramdan, Reza Habibifar and Afshin Davarpanah

Sensors 2021, 21(15), 5246; https://doi.org/10.3390/s21155246 - 03 Aug 2021

Cited by 5 | Viewed by 2513

Abstract

The power system planning problem considering system loss function, voltage profile function, the cost function of FACTS (flexible alternating current transmission system) devices, and stability function are investigated in this paper. With the growth of electronic technologies, FACTS devices have improved stability and [...] Read more.

The power system planning problem considering system loss function, voltage profile function, the cost function of FACTS (flexible alternating current transmission system) devices, and stability function are investigated in this paper. With the growth of electronic technologies, FACTS devices have improved stability and more reliable planning in reactive power (RP) planning. In addition, in modern power systems, renewable resources have an inevitable effect on power system planning. Therefore, wind resources make a complicated problem of planning due to conflicting functions and non-linear constraints. This confliction is the stochastic nature of the cost, loss, and voltage functions that cannot be summarized in function. A multi-objective hybrid algorithm is proposed to solve this problem by considering the linear and non-linear constraints that combine particle swarm optimization (PSO) and the virus colony search (VCS). VCS is a new optimization method based on viruses’ search function to destroy host cells and cause the penetration of the best virus into a cell for reproduction. In the proposed model, the PSO is used to enhance local and global search. In addition, the non-dominated sort of the Pareto criterion is used to sort the data. The optimization results on different scenarios reveal that the combined method of the proposed hybrid algorithm can improve the parameters such as convergence time, index of voltage stability, and absolute magnitude of voltage deviation, and this method can reduce the total transmission line losses. In addition, the presence of wind resources has a positive effect on the mentioned issue. Full article

(This article belongs to the Special Issue Smart IoT System for Renewable Energy Resource)

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21 pages, 7855 KiB

Open AccessEditor’s ChoiceArticle

A Low-Cost IoT System for Real-Time Monitoring of Climatic Variables and Photovoltaic Generation for Smart Grid Application

by Gustavo Costa Gomes de Melo, Igor Cavalcante Torres, Ícaro Bezzera Queiroz de Araújo, Davi Bibiano Brito and Erick de Andrade Barboza

Sensors 2021, 21(9), 3293; https://doi.org/10.3390/s21093293 - 10 May 2021

Cited by 30 | Viewed by 4932

Abstract

Monitoring and data acquisition are essential to recognize the renewable resources available on-site, evaluate electrical conversion efficiency, detect failures, and optimize electrical production. Commercial monitoring systems for the photovoltaic system are generally expensive and closed for modifications. This work proposes a low-cost real-time [...] Read more.

Monitoring and data acquisition are essential to recognize the renewable resources available on-site, evaluate electrical conversion efficiency, detect failures, and optimize electrical production. Commercial monitoring systems for the photovoltaic system are generally expensive and closed for modifications. This work proposes a low-cost real-time internet of things system for micro and mini photovoltaic generation systems that can monitor continuous voltage, continuous current, alternating power, and seven meteorological variables. The proposed system measures all relevant meteorological variables and directly acquires photovoltaic generation data from the plant (not from the inverter). The system is implemented using open software, connects to the internet without cables, stores data locally and in the cloud, and uses the network time protocol to synchronize the devices’ clocks. To the best of our knowledge, no work reported in the literature presents these features altogether. Furthermore, experiments carried out with the proposed system showed good effectiveness and reliability. This system enables fog and cloud computing in a photovoltaic system, creating a time series measurements data set, enabling the future use of machine learning to create smart photovoltaic systems. Full article

(This article belongs to the Special Issue Smart IoT System for Renewable Energy Resource)

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35 pages, 13602 KiB

Open AccessArticle

Influence of Data Sampling Frequency on Household Consumption Load Profile Features: A Case Study in Spain

by J. C. Hernandez, F. Sanchez-Sutil, A. Cano-Ortega and C. R. Baier

Sensors 2020, 20(21), 6034; https://doi.org/10.3390/s20216034 - 23 Oct 2020

Cited by 17 | Viewed by 2746

Abstract

Smart meter (SM) deployment in the residential context provides a vast amount of data of high granularity at the individual household level. In this context, the choice of temporal resolution for describing household load profile features has a crucial impact on the results [...] Read more.

Smart meter (SM) deployment in the residential context provides a vast amount of data of high granularity at the individual household level. In this context, the choice of temporal resolution for describing household load profile features has a crucial impact on the results of any action or assessment. This study presents a methodology that makes two new contributions. Firstly, it proposes periodograms along with autocorrelation and partial autocorrelation analyses and an empirical distribution-based statistical analysis, which are able to describe household consumption profile features with greater accuracy. Secondly, it proposes a framework for data collection in households at a high sampling frequency. This methodology is able to analyze the influence of data granularity on the description of household consumption profile features. Its effectiveness was confirmed in a case study of four households in Spain. The results indicate that high-resolution data should be used to consider the full range of consumption load fluctuations. Nonetheless, the accuracy of these features was found to largely depend on the load profile analyzed. Indeed, in some households, accurate descriptions were obtained with coarse-grained data. In any case, an intermediate data-resolution of 5 s showed feature characterization closer to those of 0.5 s. Full article

(This article belongs to the Special Issue Smart IoT System for Renewable Energy Resource)

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16 pages, 4119 KiB

Open AccessEditor’s ChoiceArticle

Towards Efficient Electricity Forecasting in Residential and Commercial Buildings: A Novel Hybrid CNN with a LSTM-AE based Framework

by Zulfiqar Ahmad Khan, Tanveer Hussain, Amin Ullah, Seungmin Rho, Miyoung Lee and Sung Wook Baik

Sensors 2020, 20(5), 1399; https://doi.org/10.3390/s20051399 - 04 Mar 2020

Cited by 118 | Viewed by 9280

Abstract

Due to industrialization and the rising demand for energy, global energy consumption has been rapidly increasing. Recent studies show that the biggest portion of energy is consumed in residential buildings, i.e., in European Union countries up to 40% of the total energy is [...] Read more.

Due to industrialization and the rising demand for energy, global energy consumption has been rapidly increasing. Recent studies show that the biggest portion of energy is consumed in residential buildings, i.e., in European Union countries up to 40% of the total energy is consumed by households. Most residential buildings and industrial zones are equipped with smart sensors such as metering electric sensors, that are inadequately utilized for better energy management. In this paper, we develop a hybrid convolutional neural network (CNN) with an long short-term memory autoencoder (LSTM-AE) model for future energy prediction in residential and commercial buildings. The central focus of this research work is to utilize the smart meters’ data for energy forecasting in order to enable appropriate energy management in buildings. We performed extensive research using several deep learning-based forecasting models and proposed an optimal hybrid CNN with the LSTM-AE model. To the best of our knowledge, we are the first to incorporate the aforementioned models under the umbrella of a unified framework with some utility preprocessing. Initially, the CNN model extracts features from the input data, which are then fed to the LSTM-encoder to generate encoded sequences. The encoded sequences are decoded by another following LSTM-decoder to advance it to the final dense layer for energy prediction. The experimental results using different evaluation metrics show that the proposed hybrid model works well. Also, it records the smallest value for mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) when compared to other state-of-the-art forecasting methods over the UCI residential building dataset. Furthermore, we conducted experiments on Korean commercial building data and the results indicate that our proposed hybrid model is a worthy contribution to energy forecasting. Full article

(This article belongs to the Special Issue Smart IoT System for Renewable Energy Resource)

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21 pages, 7953 KiB

Open AccessArticle

Knowledge-Based Sensors for Controlling A High-Concentration Photovoltaic Tracker

by Joaquin Canada-Bago, Jose-Angel Fernandez-Prieto, Manuel-Angel Gadeo-Martos and Pedro Perez-Higueras

Sensors 2020, 20(5), 1315; https://doi.org/10.3390/s20051315 - 28 Feb 2020

Cited by 6 | Viewed by 2214

Abstract

To reduce the cost of generated electrical energy, high-concentration photovoltaic systems have been proposed to reduce the amount of semiconductor material needed by concentrating sunlight using lenses and mirrors. Due to the concentration of energy, the use of tracker or pointing systems is [...] Read more.

To reduce the cost of generated electrical energy, high-concentration photovoltaic systems have been proposed to reduce the amount of semiconductor material needed by concentrating sunlight using lenses and mirrors. Due to the concentration of energy, the use of tracker or pointing systems is necessary in order to obtain the desired amount of electrical energy. However, a high degree of inaccuracy and imprecision is observed in the real installation of concentration photovoltaic systems. The main objective of this work is to design a knowledge-based controller for a high-concentration photovoltaic system (HCPV) tracker. The methodology proposed consists of using fuzzy rule-based systems (FRBS) and to implement the controller in a real system by means of Internet of Things (IoT) technologies. FRBS have demonstrated correct adaptation to problems having a high degree of inaccuracy and uncertainty, and IoT technology allows use of constrained resource devices, cloud computer architecture, and a platform to store and monitor the data obtained. As a result, two knowledge-based controllers are presented in this paper: the first based on a pointing device and the second based on the measure of the electrical current generated, which showed the best performance in the experiments carried out. New factors that increase imprecision and uncertainty in HCPV solar tracker installations are presented in the experiments carried out in the real installation. Full article

(This article belongs to the Special Issue Smart IoT System for Renewable Energy Resource)

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22 pages, 7937 KiB

Open AccessTechnical Note

Configurable IoT Open-Source Hardware and Software I-V Curve Tracer for Photovoltaic Generators

by Isaías González, José María Portalo and Antonio José Calderón

Sensors 2021, 21(22), 7650; https://doi.org/10.3390/s21227650 - 18 Nov 2021

Cited by 18 | Viewed by 3192

Abstract

Photovoltaic (PV) energy is a renewable energy resource which is being widely integrated in intelligent power grids, smart grids, and microgrids. To characterize and monitor the behavior of PV modules, current-voltage (I-V) curves are essential. In this regard, Internet of Things (IoT) technologies [...] Read more.

Photovoltaic (PV) energy is a renewable energy resource which is being widely integrated in intelligent power grids, smart grids, and microgrids. To characterize and monitor the behavior of PV modules, current-voltage (I-V) curves are essential. In this regard, Internet of Things (IoT) technologies provide versatile and powerful tools, constituting a modern trend in the design of sensing and data acquisition systems for I-V curve tracing. This paper presents a novel I-V curve tracer based on IoT open-source hardware and software. Namely, a Raspberry Pi microcomputer composes the hardware level, whilst the applied software comprises mariaDB, Python, and Grafana. All the tasks required for curve tracing are automated: load sweep, data acquisition, data storage, communications, and real-time visualization. Modern and legacy communication protocols are handled for seamless data exchange with a programmable logic controller and a programmable load. The development of the system is expounded, and experimental results are reported to prove the suitability and validity of the proposal. In particular, I-V curve tracing of a monocrystalline PV generator under real operating conditions is successfully conducted. Full article

(This article belongs to the Special Issue Smart IoT System for Renewable Energy Resource)

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