The current study emphasizes how important it is to have an electrostatic factor track system in order to keep track of different loads. The implementation of Energy Demand-Side Controlling (EDM) it smart grids is emp...
详细信息
This work investigates the efficiency of the process of load disaggregation, considering only the values of active power. To perform the task, we use data collected from the NILM (Non-Intrusive Load Monitoring) measur...
详细信息
This work investigates the efficiency of the process of load disaggregation, considering only the values of active power. To perform the task, we use data collected from the NILM (Non-Intrusive Load Monitoring) measurement method, presented in the Rainforest Automation Energy dataset (RAE) and Reference Energy Disagreggation dataset (redd) database. A strategy of assigning labels using combinations of equipment in use, by status ON/OFF, and also by choosing an appropriate temporal data window is discussed. Also, the performance of very well-known machine learning algorithms such as k-Nearest Neighbor (kNN), Decision Tree, and Random Forest are evaluated. The results show a very efficient and low computer complexity strategy presenting values of F1-Score above 95%, for RAE and redd database. As presented in table I, the proposed approach presents the highest F1-Score, compared to other methods in the literature, considering all appliances in the redd database. The greatest benefit of the approach consists in the possibility of applying the disaggregation process in a household without smart outlets, under the restriction that the training and test houses hold identical or similar appliances.
Mounting concerns pertaining to energy efficiency have led to the research of load monitoring. By Non-Intrusive Load Monitoring (NILM), detailed information regarding the electric energy consumed by each appliance per...
详细信息
Mounting concerns pertaining to energy efficiency have led to the research of load monitoring. By Non-Intrusive Load Monitoring (NILM), detailed information regarding the electric energy consumed by each appliance per day or per hour can be formed. The accuracy of the previous residential load monitoring approach relies heavily on the data acquisition frequency of the energy meters. It brings high overall cost issues, and furthermore, the differentiating algorithm becomes much more complicated. Based on this, we proposed a novel non-Intrusive residential load disaggregation method that only depends on the regular data acquisition speed of active power measurements. Additionally, this approach brings some novelties to the traditionally used denoising Auto-Encoder (dAE), i.e., the reconfiguration of the overlapping parts of the sliding windows. The median filter is used for the data processing of the overlapping window. Two datasets, i.e., the Reference Energy Disaggregation dataset (redd) and TraceBase, are used for test and validation. By numerical testing of the real residential data, it proves that the proposed method is superior to the traditional Factorial Hidden Markov Model (FHMM)-based approach. Furthermore, the proposed method can be used for energy data, disaggregation disregarding the brand and model of each appliance.
暂无评论