4 research outputs found
Steady State Modification Method Based On Backpropagation Neural Network For Non-Intrusive Load Monitoring (NILM)
No full textHousehold electric power sector is highlighted as one of significant contributors to national energy consumption. To reduce electric energy usage in this sector, a technique called Non-Intrusive Load Monitoring (NILM) has been developed recently. NILM is a load disaggregating and monitoring tool that can be used to identify the daily usage behavior of individual electric appliance. Different to conventional method, NILM promises the reduction of sensor deployment significantly. NILM commonly uses either transient or steady state signal. Based on load/appliance signal condition, many NILM’s research results have been published. In this paper, steady state modification method of backpropagation neural network (NN) is applied for developing NILM. We use steady state signal to disaggregate the sum of load power signal. In the proposed method, NN is explored for feature extraction of electric power consumption of individual appliance. The presented method is powerful for load power signal which has almost same value. To verify the effectiveness of proposed method, data provided by tracebase.org has been used. The presented method can be applied for local data. It is obvious from simulation results that the proposed method could improve the recognition rate of appliances until 100 %
Deep learning-based prediction of float model performance in floatplanes: A case study on lift-to-drag coefficient ratio
Get PDFDeveloping an engineering design is resource-intensive and time-consuming, particularly for the floats of a floatplane design, due to its complexity and limited testing facilities. Intelligent-based computational design (IBCD) techniques, which integrate computational design techniques and machine learning (ML) algorithms, offer a solution to reduce required testing by providing predictions. This paper proposes a deep learning (DL)-based IBCD method for modeling floats' lift-to-drag coefficient ratio (CL/CD), where DL is one of the most powerful ML. The proposed method consists of two phases: hyper-parameter optimization and DL model training and evaluation. A genetic algorithm (GA) is employed in the first phase to explore complex hyper-parameter combinations efficiently. Evaluation of the predicted CL/CD of the floats using the DL model resulted in a satisfactory R-squared of 0.9329 and the lowest mean squared error (MSE) of 0,001536. These results demonstrate the ability of DL model to predict the float's performance accurately and can facilitate further design optimization. Thus, the proposed method can offer a time-efficient and cost-effective solution for predicting float performance, aiding in optimizing floatplane designs and enhancing their functionalities
Predicting water resistance and pitching angle during take-off: an artificial neural network approach
Get PDFThis research addresses the challenges faced by seaplanes and amphibious aircraft during takeoff and landing on water, emphasizing the limitations and costs associated with traditional towing tank tests and computational fluid dynamics (CFD) simulations. The study proposes an innovative approach that employs artificial neural networks (ANN) to predict water resistance and pitching angle during amphibious aircraft take-off, minimizing the reliance on expensive towing tank tests. The ANN models are developed and optimized using Bayesian optimization, showcasing improved accuracy in predicting water resistance and pitching angle. The research demonstrates the potential of machine learning, specifically ANNs, to significantly reduce the need for costly experimental tests, providing an efficient alternative for designing amphibious aircraft. The results indicate high accuracy in predicting water resistance and pitching angle, offering substantial time and resource savings during the experimental phase. However, the study highlights the need for model adaptation for different designs and test variations to enhance overall applicability
Application of artificial intelligence in emission prediction for hybrid electric vehicles: integrating ANN and GPR
Full text linkIn recent years, hybrid electric vehicles (HEVs) have emerged as a promising solution to mitigate vehicular emissions and improve fuel efficiency. This study focuses on the Toyota Prius HEV, employing advanced artificial neural networks (ANN) and Gaussian process regression (GPR) to develop a predictive model for vehicle emissions. The model considers multiple pollutants, including carbon monoxide (CO), carbon dioxide (CO₂), hydrocarbons (HC), and nitrogen oxides (NOx), measured under diverse driving conditions. The ANN model predicts emission trends, while GPR estimates prediction uncertainty, enhancing the model’s robustness. The GPR models achieved uncertainty levels of ±0.829 ppm for CO, ±9.978 ppm for HC, ±0.144 ppm for NOx, and ±411.256 ppm for CO₂, respectively, underscoring the robustness of the integrated approach for emission prediction. This research aims to support the development of more sustainable vehicle technologies and inform policy making for environmental sustainability (e.g., Euro 6/Euro 7 standards). Overall, the study addresses how artificial intelligence (AI) can be utilized to achieve accurate multi-pollutant emission predictions in HEVs. The findings reveal that an integrated ANN-GPR approach yields superior predictive performance (R² values approaching 1.0) with quantifiable uncertainty, outperforming a stand-alone ANN model and providing a robust solution to the emission prediction challenge
