Browsing by Author "Palacios-Garcia, Emilio J."

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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: 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.