15 research outputs found
Improvement on PM-10 Forecast by Using Hybrid ARIMAX and Neural Networks Model for the Summer Season in Chiang Mai
AbstractSince the air monitoring stations do not provide the relation between other toxic gas and meteorological parameters with the particulate matter up to 10 micrometer or PM-10. The influence of meteorological as well as correlation with other toxic gas is investigated and used them to forecast PM-10 in the case of Chiang Mai province of Thailand. In this paper an attempt to develop hybrid models of an Autoregressive Integrated Moving Average (ARIMA) model with other exogenous variables (ARIMAX) and Neural Networks (NNs), the two hybrid models, i.e. hybrid ARIMAX-NNs model and hybrid NNs-ARIMAX model were implemented to forecast PM-10 for highly season during January-April of Chiang Mai Province. Simulation results of hybrid model are compared with the results of ARIMA, ARIMAX and NNs model. The experimental results demonstrated that the hybrid NNs-ARIMAX model outperformed best over the hybrid ARIMAX-NNs model, ARIMAX model, NNs model, and ARIMA model respectively. In this case study and maybe other cases, it has proved that the NNs model should be priori captured and filtered the non-stationary non-linear component while the fully linearly stationary residuals were accurately predicted by ARIMAX model later
Performance of the hybrid MLPNN based VE (hMLPNN-VE) for the nonlinear PMR channels
This paper proposes a hybrid of multilayer perceptron neural network (MLPNN) and Volterra equalizer (VE) denoted hMLPNN-VE in nonlinear perpendicular magnetic recording (PMR) channels. The proposed detector integrates the nonlinear product terms of the delayed readback signals generated from the VE into the nonlinear processing of the MLPNN. The detection performance comparison is evaluated in terms of the tradeoff between the bit error rate (BER), complexity and reliability for a nonlinear Volterra channel at high normalized recording density. The proposed hMLPNN-VE outperforms MLPNN based equalizer (MLPNNE), VE and the conventional partial response maximum likelihood (PRML) detector
Improvement Performances of PV Water Pumping System Using MPPT-based Modified P&O Controller: Modeling, Setting Experimental Package and Analysis
āļāļēāļāļ§āļīāļāļąāļĒāļāļĩāđāđāļāđāļŠāļĢāđāļēāļāđāļāļāļāļģāļĨāļāļāđāļĨāļ°āļāļļāļāļāļāļĨāļāļāļāļĒāđāļēāļāļāđāļēāļĒāđāļāļ·āđāļāļāļāļŠāļāļāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāļāļāļāļĢāļ°āļāļāļāļąāđāļĄāļāđāļģāļāļĨāļąāļāļāļēāļāđāļŠāļāļāļēāļāļīāļāļĒāđ (Photovoltaic Water Pumping System; PVWPS) āļāļāļēāļāđāļĨāđāļāļāļĩāđāđāļĄāđāđāļāđāđāļāļāđāļāļāļĢāļĩāđ āđāļāļĒāđāļāđāļāļąāļ§āļāļ§āļāļāļļāļĄāđāļāļāļāļēāļĢāļĢāļāļāļ§āļāđāļĨāļ°āļŠāļąāļāđāļāļāļāļĩāđāļāļđāļāļāļĢāļąāļ (Modified Perturb and Observe Controller; MP&O) āļāļķāđāļāđāļĄāđāļāļąāļāļāđāļāļ āđāļĨāļ°āļāļēāļĻāļąāļĒāļŦāļĨāļąāļāļāļēāļĢāļāļīāļāļāļēāļĄāļāļļāļāļāļĩāđāđāļŦāđāļāļģāļĨāļąāļāđāļāļāđāļēāļŠāļđāļāļŠāļļāļ (Maximum Power Point Tracking Method; MPPT) āđāļāļ·āđāļāđāļāļīāđāļĄāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāļāļēāļĢāđāļāļĨāļāļāļĨāļąāļāļāļēāļāļāļāļāļĢāļ°āļāļ (Energy Conversion Efficiency) āļāļķāđāļāđāļāđāļāļāļļāļāļŠāļĢāļĢāļāļŠāļģāļāļąāļāđāļāļāļēāļĢāļāļĢāļ°āļĒāļļāļāļāđāđāļāđāđāļāļāđāļāđāļĨāļĒāļĩāļāļĨāļąāļāļāļēāļāđāļŠāļāļāļēāļāļīāļāļĒāđāļŠāļģāļŦāļĢāļąāļāļĢāļ°āļāļāđāļāļāđāļēāļāļģāļĨāļąāļāļāļąāļ§āļāļ§āļāļāļļāļĄāļāļĩāđāļāļģāđāļŠāļāļāļāļ°āļŠāļĢāđāļēāļāļŠāļąāļāļāļēāļāļāļ§āļāļāļļāļĄāļāļĩāđāļĄāļĩāļāļāļēāļāļāļąāđāļāļāļēāļĢāļāļ§āļāļāļļāļĄāđāļĄāđāļāļāļāļĩāđāđāļŦāđāļāļąāļāļ§āļāļāļĢāļāļąāļāļāļāļāđāļ§āļāļĢāđāđāļāļāļĢāđ (Buck Converter) āđāļāļ·āđāļāđāļĨāļ·āđāļāļāļāļļāļāļāļģāļāļēāļāđāļāļĒāļąāļāļāļļāļāļāļĩāđāđāļŦāđāļāļģāļĨāļąāļāđāļāļāđāļēāļŠāļđāļāļŠāļļāļāļāļāļāđāļāļĨāļĨāđāđāļŠāļāļāļēāļāļīāļāļĒāđāļāļķāđāļāļāļ°āļāļđāļāļŠāđāļāļāđāļēāļāđāļāļĒāļąāļāđāļŦāļĨāļāļāļ·āļāļĄāļāđāļāļāļĢāđāđāļĨāļ°āļāļąāđāļĄāļāđāļģ āļĢāļ°āļāļ PVWPS-MPPT-MP&O āļāļđāļāļāļģāļĨāļāļāđāļāļĒāđāļāđāđāļāļĢāđāļāļĢāļĄ Matlab/Simulink āđāļĨāļ°āļāļ§āļāļŠāļāļāļāļĨāļāļĩāđāđāļāđāļāļēāļāđāļāļāļāļģāļĨāļāļāļāđāļ§āļĒāļāļļāļāļāļāļĨāļāļāļāđāļāđāļāļāļĢāļ°āļāļāļāļąāđāļĄāļāđāļģāļāļĨāļąāļāļāļēāļāđāļŠāļāļāļēāļāļīāļāļĒāđāļāļāļēāļ 130 āļ§āļąāļāļāđ āļāļĩāđāļāļđāļāļāļ§āļāļāļļāļĄāļāđāļēāļāđāļĄāđāļāļĢāļāļāļāđāļāļĢāļĨāđāļĨāļāļĢāđāđāļāļ Arduino āļ āļēāļĒāđāļāđāļŠāļ āļēāļ§āļ°āļāļĢāļīāļāļāļāļāļāļ§āļēāļĄāđāļāđāļĄāđāļŠāļāļāļēāļāļīāļāļĒāđāđāļĨāļ°āļāļļāļāļŦāļ āļđāļĄāļīāļāļāļāđāļāļāđāļāļĨāļĨāđāļāļĩāđāđāļāļĨāļĩāđāļĒāļāđāļāļĨāļāļāļĨāļāļāļ§āļąāļ āļāļĨāļāļēāļĢāļāļāļŠāļāļāļāļāļ§āđāļē āļāļąāļ§āļāļ§āļāļāļļāļĄāļāļĩāđāļāļģāđāļŠāļāļāļāđāļ§āļĒāđāļāļīāđāļĄāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāļāļēāļĢāđāļāđāļāļĨāļąāļāļāļēāļ (Energy Utilization Efficiency) āđāļĨāļ°āļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāļāļāļāđāļāļĨāļĨāđāđāļŠāļāļāļēāļāļīāļāļĒāđ (PV Efficiency) āļāļķāļ 75.7 āđāļāļāļĢāđāđāļāđāļāļāđ āđāļĨāļ° 11.8 āđāļāļāļĢāđāđāļāđāļāļāđ āļāļēāļĄāļĨāļģāļāļąāļ āļāļģāđāļŦāđāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāļĢāļ§āļĄāļāļāļ PVWPS āđāļāļīāđāļĄāļāļķāđāļ 41 āđāļāļāļĢāđāđāļāđāļāļāđāđāļāļĩāļĒāļāļāļąāļ PVWPS āļāļĩāđāđāļĄāđāđāļāđāļāļąāļ§āļāļ§āļāļāļļāļĄA very low energy conversion efficiency of the photovoltaic (PV) technology is the main barrier in developing the PV power applications. To address the problem, in this work, a low-cost and simple converter-controller based on the Maximum Power Point Tracking (MPPT) technique is integrated into the small-scale 130-W PV Water Pumping System (PVWPS) without using battery storage. The MPPT-based Modified Perturb and Observe (MP&O) method based on variable step-size control is proposed and demonstrated through Matlab/Simulink software. To validate and verify the simulation model, the MPPT-MP&O is implemented using Arduino microcontroller and applied to the prototype PVWPS. When carried out under the actual weather conditions, as a result, it helps to increase the energy utilization efficiency and the PV efficiency up to 75.7% and 11.8%, respectively, and consequently improves the global efficiency by 41% over the PVWPS without a controller
Optimal Hybrid Neuro-fuzzy Based Controller Using MOGA for Photovoltaic (PV) Battery Charging System
New hybrid statistical method and machine learning for PM10 prediction
The objective of this research is to propose new hybrid model by combining Time Series Regression (TSR) as statistical method and Feedforward Neural Network (FFNN) or Long Short-Term Memory (LSTM) as machine learning for PM10 prediction at three SUF stations in Surabaya City, Indonesia. TSR as an individual linear model is used to capture trend and seasonal pattern. Whereas, FFNN or LSTM is employed to handle nonlinear pattern. Thus, this research proposes two hybrid models, i.e. hybrid TSR-FFNN and hybrid TSR-LSTM. Data about PM10 level that be observed half hourly at three SUF stations in Surabaya are used as case study. The performance of these two hybrid models will be compared with several individual models such as ARIMA, FFNN, and LSTM by using sMAPEP. The results at identification step showed that the data has double seasonal patterns, i.e. daily and weekly seasonality. Moreover, the forecast accuracy comparison showed that hybrid TSR-FFNN produced more accurate PM10 forecast than other methods at SUF 7, whereas FFNN yielded more accurate forecast at SUF 1 and SUF 7. These results show that FFNN as an individual nonlinear model produce better forecast than TSR and ARIMA as an individual linear model. It indicates that the PM10 in Surabaya tend to have nonlinear pattern. Moreover, these results are also in line with the results of M3 competition, i.e. more complex method do not necessary produce better forecast than a simpler one