Browsing by Author "Vasquez, Juan C."

Now showing 1 - 7 of 7

Citation - WoS: 37
Citation - Scopus: 43
Data-Driven Ship Berthing Forecasting for Cold Ironing in Maritime Transportation
(Elsevier Ltd, 2022) Bakar, Nur Najihah Abu; Bazmohammadi, Najmeh; Çimen, Halil; Uyanık, Tayfun; Vasquez, Juan C.; Guerrero, Josep M.
Cold ironing (CI) is an electrification alternative in the maritime sector used to reduce shipborne emissions by switching from fuel to electricity when a ship docks at a port. During the ship's berthing mode of operation, accurately estimating the berthing duration could assist the port operator to manage the berth allocation and energy scheduling optimally. However, the involvement of multiple input parameters with a large dataset requires a suitable handling method. Thus, this paper proposed a data-driven approach for ship berthing forecasting of cold ironing with various models such as artificial neural networks, multiple linear regression, random forest, decision tree, and extreme gradient boosting. Meanwhile, RMSE and MAE are two main indicators applied to assess forecasting accuracy. The simulation-based result shows that the artificial neural network outperforms all other models with the lowest error performance of RMSE (3.1343) and MAE (0.2548), suggesting its capability to handle nonlinearities in complex forecasting problems of port activity. The high accuracy of forecasting output in this study, which is berthing duration contributes to close estimation of two info: 1) CI power consumption and 2) departure time of the ship. This information is vital to the port operator to be used in the energy management system (EMS) as well as in the berth allocation problem (BAP). © 2022 The Author(s)
Citation - WoS: 21
Citation - Scopus: 32
Deep Learning-Based Probabilistic Autoencoder for Residential Energy Disaggregation: an Adversarial Approach
(Ieee-Inst Electrical Electronics Engineers Inc, 2022) Çimen, Halil; Wu, Ying; Wu, Yanpeng; Terriche, Yacine; Vasquez, Juan C.; Guerrero, Josep M.
Energy disaggregation is the process of disaggregating a household's total energy consumption into its appliance-level components. One of the limitations of energy disaggregation is its generalization capacity, which can be defined as the ability of the model to analyze new households. In this article, a new energy disaggregation approach based on adversarial autoencoder (AAE) is proposed to create a generative model and enhance the generalization capacity. The proposed method has a probabilistic structure to handle uncertainties in the unseen data. By transforming the latent space from a deterministic structure to a Gaussian prior distribution, AAEs decoder transforms into a generative model. The proposed approach is validated through experimental tests using two different datasets. The experimental results exhibit a 55% MAE performance increase compared to deterministic models and 7% compared to probabilistic models. In addition, considering the predictions made when the appliances are on, the AAE improves the performance by 16% for UKDALE and 36% for REDD dataset compared to the state-of-art models. Moreover, the online analysis performance of AAE is examined in detail, and the disadvantages of instant predictions and the possible solutions are extensively discussed.
Citation - WoS: 3
Citation - Scopus: 6
A Dual-input Multi-label Classification Approach for Non-Intrusive Load Monitoring via Deep Learning
(IEEE, 2020) Çimen, Halil; Palacios-Garcia, Emilio J.; Çetinkaya, Nurettin; Vasquez, Juan C.; Guerrero, Josep M.
Non-intrusive load monitoring (NILM) is the process of obtaining appliance-level data from users' total electricity consumption data. These data can be of great benefit, especially in demand response applications. In this paper, a multi-label classification for NILM based on a two-input gated recurrent unit (GRU) is presented. Since the presented method is designed with a multi-label approach, great savings in training time are achieved. While a separate model is trained for each appliance in the literature, only one model is trained in the proposed model. Besides, the model was trained using two different inputs. The first is the total active power value consumed by the whole house. The second input is the Spikes obtained by analyzing this active power consumption. Simply put, spikes are obtained by analyzing the instant power changes in active power. Both inputs are evaluated with a convolutional layer and necessary features are extracted. Obtained features are fed into the GRU to be able to analyze time-dependent changes. The simulation results show that an additional input can slightly improve the analysis accuracy. Besides, it was found that the second input is useful especially in the analysis of short-term devices.
Citation - WoS: 1
Citation - Scopus: 2
Generalization Capacity Analysis of Non-Intrusive Load Monitoring Using Deep Learning
(Ieee, 2020) Çimen, Halil; Palacios-Garcia, Emilio J.; Çetinkaya, Nurettin; Kolbak, Morten; Sciume, Giuseppe; Vasquez, Juan C.; Guerrero, Josep M.
Appliance Load Monitoring is a technique used to monitor devices existing in homes, industry or naval vessels. Acquisition of device-level data can provide great benefits in many areas such as energy management, demand response, and load forecasting. However, the monitoring process is often provided with a costly installation, as it requires a large number of sensors and a data center. Non-Intrusive Load Monitoring (NILM) is an alternative and cost-efficient load monitoring solution. Simply put, NILM is the process of obtaining device-level data by analyzing the aggregated data read from the main meter that measures the electricity consumption of the whole house. Before NILM analysis is performed, the load patterns of the appliances are usually modeled individually. In general, one model for each appliance is modeled even if the appliance has more than one operating program such as washing machine and oven. Therefore, when the appliance operates in other programs, the accuracy of NILM analysis decreases. In this paper, an appliance-based NILM analysis has been made considering the appliances having multiple operating programs. In order to increase the accuracy of NILM analysis, several deep learning methods, which are the most important data-driven technique of recent times, are used. Developed models were tested in IoT Microgrid Laboratory environment.
Citation - WoS: 113
Citation - Scopus: 147
A Microgrid Energy Management System Based on Non-Intrusive Load Monitoring Via Multitask Learning
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021) Çimen, Halil; Çetinkaya, Nurettin; Vasquez, Juan C.; Guerrero, Josep M.
Non-intrusive load monitoring (NILM) enables to understand the appliance-level behavior of the consumers by using only smart meter data, and it mitigates the requirements such as high-cost sensors, maintenance/update and provides a cost-effective solution. This article presents an efficient NILM-based energy management system (EMS) for residential microgrids. Firstly, smart meter data are analyzed with a multi-task deep neural network-based approach and the appliance-level information of the consumers is extracted. Both consumption and operating status of the appliances are obtained. Afterward, the energy consumption behaviors of the end-users are analyzed using these data. Accordingly, average power consumption, operation cycles, preferred usage periods, and daily usage frequency of the appliances were obtained with an average accuracy of more than 90%. The obtained results were integrated into an EMS to create an efficient and user-centered microgrid operation. The developed model not only provided the optimum dispatch of distributed generation plants in the microgrid but also scheduled the controllable loads taking into account customers' satisfaction. It was demonstrated with the help of simulation that the proposed NILM-based EMS model improves the operation cost/customer satisfaction ratio between 45% and 65% compared to a traditional EMS.
Citation - WoS: 10
Citation - Scopus: 12
Smart-Building Applications: Deep Learning-Based, Real-Time Load Monitoring
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021) Çimen, Halil; Palacios-Garcia, Emilio J.; Kolbaek, Morten; Çetinkaya, Nurettin; Vasquez, Juan C.; Guerrero, Josep M.
Google's Director of Research Peter Norvig said, We don't have better algorithms than anyone else; we just have more data. This inspiring statement shows that having more data is directly related to better decision making and foresight about the future. With the development of Internet of Things (IoT) technology, it is now much easier to gather data. Technological tools, such as social media websites, smartphones, and security cameras, can be considered as data generators. When the focus is shifted to the energy field, these generators are smart meters.
Citation - WoS: 119
Citation - Scopus: 167
Towards Collective Energy Community: Potential Roles of Microgrid and Blockchain To Go Beyond P2p Energy Trading
(Elsevier Ltd, 2022) Wu, Ying; Wu, Yanpeng; Çimen, Hilal; Vasquez, Juan C.; Guerrero, Josep M.
Decarbonisation of energy sector is crucial to deliver the future net zero energy system with promoting and facilitating the large-scale electrification of end-user sectors. It is necessary to provide sustainable, cost-effective, resilient and scalable energy solutions to exploit the power of citizens to contribute to the clean energy transition, increasing the flexibility of the overall energy system. Energy community, as the new actor, create an integrated pan energy market by bringing together the local consumers and energy market players. However, diversity of energy community brings huge challenges in integration of decentralized renewables with regulated framework, interaction of decentralized marketplaces, as well as interoperability of the cross-border energy sectors with privacy, security and incentives. This paper intends to provide an in-depth investigation on the role of microgrid and blockchain, alone and together, in facilitating the energy community as the “enabling framework” to boost the potential solutions of electrification in the transportation, building, and industrial sectors, as well as rural/remote areas and islands towards a networking green ecosystem. This paper serves as a comprehensive reference to understand the modern microgrid on its control and communication technology with integration of blockchain services in promoting the techno-socio-economic innovations for the restructuring of the sustainable energy supply chain. © 2022 The Author(s)