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Prof. Dr. Chiou-Jye Huang
Providence University

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Research Keywords & Expertise

0 Artificial Intelligence
0 Deep Learning
0 Machine Learning
0 Renewable and Sustainable Energy
0 Time Series Analysis and Forecasting

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Machine Learning
Artificial Intelligence
Deep Learning
Time Series Analysis and Forecasting

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Short Biography

His research interests include big data and time series analysis, machine learning and deep learning applications on the Internet of Energy (IoE) and environmental science, especially in renewable energy, as well as electricity load demand, electricity prices, solar radiance, photovoltaic power, and PM2.5 forecasting, and photovoltaic power plants planning, design and operation maintenance management.

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Journal article
Published: 04 February 2021 in Sustainability
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Electricity load forecasting is one of the hot concerns of the current electricity market, and many forecasting models are proposed to satisfy the market participants’ needs. Most of the models have the shortcomings of large computation or low precision. To address this problem, a novel deep learning and data processing ensemble model called SELNet is proposed. We performed an experiment with this model; the experiment consisted of two parts: data processing and load forecasting. In the data processing part, the autocorrelation function (ACF) was used to analyze the raw data on the electricity load and determine the data to be input into the model. The variational mode decomposition (VMD) algorithm was used to decompose the electricity load raw-data into a set of relatively stable modes named intrinsic mode functions (IMFs). According to the time distribution and time lag determined using the ACF, the input of the model was reshaped into a 24 × 7 × 8 matrix M, where 24, 7, and 8 represent 24 h, 7 days, and 8 IMFs, respectively. In the load forecasting part, a two-dimensional convolutional neural network (2D-CNN) was used to extract features from the matrix M. The improved reshaped layer was used to reshape the extracted features according to the time order. A temporal convolutional network was then employed to learn the reshaped time-series features and combined with the fully connected layer to complete the prediction. Finally, the performance of the model was verified in the Eastern Electricity Market of Texas. To demonstrate the effectiveness of the proposed model data processing and load forecasting, we compared it with the gated recurrent unit (GRU), TCN, VMD-TCN, and VMD-CNN models. The TCN exhibited better performance than the GRU in load forecasting. The mean absolute percentage error (MAPE) of the TCN, which was over 5%, was less than that of the GRU. Following the addition of VMD to the TCN, the basic performance of the model was 2–3%. A comparison between the SELNet model and the VMD-TCN model indicated that the application of a 2D-CNN improves the forecast performance, with only a few samples having an MAPE of over 4%. The model’s prediction effect in each season is discussed, and it was found that the proposed model can achieve high-precision prediction in each season.

ACS Style

Yamin Shen; Yuxuan Ma; Simin Deng; Chiou-Jye Huang; Ping-Huan Kuo. An Ensemble Model Based on Deep Learning and Data Preprocessing for Short-Term Electrical Load Forecasting. Sustainability 2021, 13, 1694 .

AMA Style

Yamin Shen, Yuxuan Ma, Simin Deng, Chiou-Jye Huang, Ping-Huan Kuo. An Ensemble Model Based on Deep Learning and Data Preprocessing for Short-Term Electrical Load Forecasting. Sustainability. 2021; 13 (4):1694.

Chicago/Turabian Style

Yamin Shen; Yuxuan Ma; Simin Deng; Chiou-Jye Huang; Ping-Huan Kuo. 2021. "An Ensemble Model Based on Deep Learning and Data Preprocessing for Short-Term Electrical Load Forecasting." Sustainability 13, no. 4: 1694.

Research article
Published: 09 September 2020 in International Journal of Energy Research
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A Ubiquitous Power Internet of Things is fundamentally an Internet of Things, but focused upon power systems. Being able to predict these prices accurately may help with the identification of customer needs and the effective regulation of the power grid by power producers. It may also help electric power traders to manage risks, make correct decisions, and obtain more benefits. In this paper, a novel hybrid model is proposed for short‐term electricity price prediction. The model consists of three algorithms: Variational Mode Decomposition (VMD); a Convolutional Neural Network (CNN); and Gated Recurrent Unit (GRU). This is called SEPNet for convenience. The annual electricity price data is divided into seasons because of seasonal differences in the time series of electricity prices. The VMD algorithm is used to decompose the complex time series of electricity prices into intrinsic mode functions (IMFs) with different center frequencies. The CNN is used to further extract the time‐domain features for all the intrinsic model functions in the VMD domain. The GRU is then employed to process and learn the time‐domain features extracted by the CNN, leading to the final prediction. A comparison is made with five models, such as LSTM, CNN, VMD‐CNN, BP, VMD‐ELMAN. The results showed that the proposed model had the best performance, and it was found that using VMD can improve the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) for the four seasons by 84% and 81%, respectively. The addition of GRU in the SEPNet model further improved the MAPE and RMSE by 19% and 25%, respectively. Including CNN and VMD‐CNN, that shows that the proposed model has the best performance. The MAPE and RMSE for the four seasonal averages are 0.730% and 0.453, respectively. This confirms that the SEPNet model has the feasibility and high accuracy to predict short‐term electricity prices.

ACS Style

Chiou-Jye Huang; Yamin Shen; Yung‐Hsiang Chen; Hsin‐Chuan Chen. A novel hybrid deep neural network model for short‐term electricity price forecasting. International Journal of Energy Research 2020, 45, 2511 -2532.

AMA Style

Chiou-Jye Huang, Yamin Shen, Yung‐Hsiang Chen, Hsin‐Chuan Chen. A novel hybrid deep neural network model for short‐term electricity price forecasting. International Journal of Energy Research. 2020; 45 (2):2511-2532.

Chicago/Turabian Style

Chiou-Jye Huang; Yamin Shen; Yung‐Hsiang Chen; Hsin‐Chuan Chen. 2020. "A novel hybrid deep neural network model for short‐term electricity price forecasting." International Journal of Energy Research 45, no. 2: 2511-2532.

Other
Published: 05 May 2020
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The coronavirus disease 2019 pandemic continues as of March 26 and spread to Europe on approximately February 24. A report from April 29 revealed 1.26 million confirmed cases and 125 928 deaths in Europe. This study proposed a novel deep neural network framework, COVID-19Net, which parallelly combines a convolutional neural network (CNN) and bidirectional gated recurrent units (GRUs). Three European countries with severe outbreaks were studied—Germany, Italy, and Spain—to extract spatiotemporal feature and predict the number of confirmed cases. The prediction results acquired from COVID-19Net were compared to those obtained using a CNN, GRU, and CNN-GRU. The mean absolute error, mean absolute percentage error, and root mean square error, which are commonly used model assessment indices, were used to compare the accuracy of the models. The results verified that COVID-19Net was notably more accurate than the other models. The mean absolute percentage error generated by COVID-19Net was 1.447 for Germany, 1.801 for Italy, and 2.828 for Spain, which were considerably lower than those of the other models. This indicated that the proposed framework can accurately predict the accumulated number of confirmed cases in the three countries and serve as a crucial reference for devising public health strategies.

ACS Style

Chiou-Jye Huang; Yamin Shen; Ping-Huan Kuo; Yung-Hsiang Chen. Novel Spatiotemporal Feature Extraction Parallel Deep Neural Network for Forecasting Confirmed Cases of Coronavirus Disease 2019. 2020, 1 .

AMA Style

Chiou-Jye Huang, Yamin Shen, Ping-Huan Kuo, Yung-Hsiang Chen. Novel Spatiotemporal Feature Extraction Parallel Deep Neural Network for Forecasting Confirmed Cases of Coronavirus Disease 2019. . 2020; ():1.

Chicago/Turabian Style

Chiou-Jye Huang; Yamin Shen; Ping-Huan Kuo; Yung-Hsiang Chen. 2020. "Novel Spatiotemporal Feature Extraction Parallel Deep Neural Network for Forecasting Confirmed Cases of Coronavirus Disease 2019." , no. : 1.

Other
Published: 27 March 2020
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COVID-19 is spreading all across the globe. Up until March 23, 2020, the confirmed cases in 173 countries and regions of the globe had surpassed 346,000, and more than 14,700 deaths had resulted. The confirmed cases outside of China had also reached over 81,000, with over 3,200 deaths. In this study, a Convolutional Neural Network (CNN) was proposed to analyze and predict the number of confirmed cases. Several cities with the most confirmed cases in China were the focus of this study, and a COVID-19 forecasting model, based on the CNN deep neural network method, was proposed. To compare the overall efficacies of different algorithms, the indicators of mean absolute error and root mean square error were applied in the experiment of this study. The experiment results indicated that compared with other deep learning methods, the CNN model proposed in this study has the greatest prediction efficacy. The feasibility and practicality of the model in predicting the cumulative number of COVID-19 confirmed cases were also verified in this study.

ACS Style

Chiou-Jye Huang; Yung-Hsiang Chen; Yuxuan Ma; Ping-Huan Kuo. Multiple-Input Deep Convolutional Neural Network Model for COVID-19 Forecasting in China. 2020, 1 .

AMA Style

Chiou-Jye Huang, Yung-Hsiang Chen, Yuxuan Ma, Ping-Huan Kuo. Multiple-Input Deep Convolutional Neural Network Model for COVID-19 Forecasting in China. . 2020; ():1.

Chicago/Turabian Style

Chiou-Jye Huang; Yung-Hsiang Chen; Yuxuan Ma; Ping-Huan Kuo. 2020. "Multiple-Input Deep Convolutional Neural Network Model for COVID-19 Forecasting in China." , no. : 1.

Journal article
Published: 25 October 2019 in Sensors
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The computer is an important medium that allows people to connect to the internet. However, people with disabilities are unable to use a computer mouse and thus cannot enjoy internet benefits. Nowadays, there are various types of assistive technologies for controlling a computer mouse, but they all have some operational inconveniences. In this paper, we propose an innovative blowing-controlled mouse assistive tool to replace the conventional hand-controlled mouse. Its main contribution is that it uses microphones to induce small signals through the principle of airflow vibration, and it then converts the received signal into the corresponding pulse width. The co-design of software programming enables various mouse functions to be implemented by the identification of the blowing pulse width of multiple microphones. The proposed tool is evaluated experimentally, and the experimental results show that the average identification rate of the proposed mouse is over 85%. Additionally, compared with the other mouse assistive tools, the proposed mouse has the benefits of low cost and humanized operation. Therefore, the proposed blowing control method can not only improve the life quality of people with disabilities but also overcome the disadvantages of existing assistive tools.

ACS Style

Hsin-Chuan Chen; Chiou-Jye Huang; Wei-Ru Tsai; Che-Lin Hsieh. A Computer Mouse Using Blowing Sensors Intended for People with Disabilities. Sensors 2019, 19, 4638 .

AMA Style

Hsin-Chuan Chen, Chiou-Jye Huang, Wei-Ru Tsai, Che-Lin Hsieh. A Computer Mouse Using Blowing Sensors Intended for People with Disabilities. Sensors. 2019; 19 (21):4638.

Chicago/Turabian Style

Hsin-Chuan Chen; Chiou-Jye Huang; Wei-Ru Tsai; Che-Lin Hsieh. 2019. "A Computer Mouse Using Blowing Sensors Intended for People with Disabilities." Sensors 19, no. 21: 4638.

Journal article
Published: 06 June 2019 in IEEE Access
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With the fast expansion of renewable energy system installed capacity in recent years, the availability, stability and quality of smart grids has become increasingly important. Renewable energy output forecasting applications has also been developing rapidly in recent years, such techniques have particularly been applied in the fields of wind and solar photovoltaic (PV). In the case of solar PV output forecasting, many applications have been performed with machine learning and hybrid techniques. In this paper, we propose a high-precision deep neural network model named PVPNet to forecast PV system output power. The methodology behind the proposed model is based on deep neural networks, the model is able to generate a 24-hour probabilistic and deterministic forecasting of PV power output, based on meteorological information such as temperature, solar radiation and historical PV system output data. The forecasting accuracy of PVPNet is determined by the mean absolute error (MAE) and root mean square error (RMSE) values. Results from the experiments show that the MAE and RMSE of the proposed algorithm are 109.4845 and 163.1513, respectively. The results prove that the prediction accuracy of PVPNet outperforms other benchmark models, the algorithm also effectively predicts complex time series with a high degree of volatility and irregularity.

ACS Style

Chiou-Jye Huang; Ping-Huan Kuo. Multiple-Input Deep Convolutional Neural Network Model for Short-Term Photovoltaic Power Forecasting. IEEE Access 2019, 7, 74822 -74834.

AMA Style

Chiou-Jye Huang, Ping-Huan Kuo. Multiple-Input Deep Convolutional Neural Network Model for Short-Term Photovoltaic Power Forecasting. IEEE Access. 2019; 7 (99):74822-74834.

Chicago/Turabian Style

Chiou-Jye Huang; Ping-Huan Kuo. 2019. "Multiple-Input Deep Convolutional Neural Network Model for Short-Term Photovoltaic Power Forecasting." IEEE Access 7, no. 99: 74822-74834.

Technical note
Published: 13 February 2019 in Energies
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In many industries and medical power system applications, dual power source design is often used to ensure that the equipment runs continuously, even when the main power supply is shut down. However, the voltage feedback between two independent power supplies and low loss output are critical issues for the system energy dissipation. Without using a dedicated chip, a new mutual blocking control technology is proposed in this paper to effectively solve the problem of voltage feedback existing in the conventional dual power system. In addition, without adding much hardware cost, the proposed dual power switch design can completely avoid voltage feedback and achieve a low voltage loss of about 30 mV when the load current is less than 0.5 A.

ACS Style

Hsin-Chuan Chen; Ping-Huan Kuo; Chiou-Jye Huang. A Mutual Blocking Technology Applied to Dual Power Source Switching Control. Energies 2019, 12, 576 .

AMA Style

Hsin-Chuan Chen, Ping-Huan Kuo, Chiou-Jye Huang. A Mutual Blocking Technology Applied to Dual Power Source Switching Control. Energies. 2019; 12 (4):576.

Chicago/Turabian Style

Hsin-Chuan Chen; Ping-Huan Kuo; Chiou-Jye Huang. 2019. "A Mutual Blocking Technology Applied to Dual Power Source Switching Control." Energies 12, no. 4: 576.

Journal article
Published: 22 January 2019 in Applied Sciences
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Theo Jansen linkage is an appealing mechanism to implement a bio-inspired motion for a legged robot. The oval orbit that is generated by the Theo Jansen linkage, possessing a transversal axis longer than a lateral axis, achieves energy efficient walking comparing to the circular orbit that is generated by the four-bar linkage. However, the ensemble of its links can produce different patterns of orbits other than oval orbits, some of which are not qualified to be the foot trajectories. It is vital to give a guideline, to which one can refer, to ensure the design of a Theo Jansen leg always possessing its eligibility. In this paper, the machine learning technique, called SVM (Support Vector Machine) along with machine vision serving as a classifier to distinguish desired trajectories from undesired ones, is employed and two databases gathering all eligible data concerned with properties of orbits and dimensions of Theo Jansen linkages are established. Based upon SVM to delimit the eligible designs, one can seek the improvement of a Theo Jansen linkage by resizing its links without rendering an ineligible design. The ensemble dimensions of Theo Jansen linkage can be determined by searching the orbits in compliance with the specification of obliqueness and slenderness from the database of properties and using their correspondent identity numbers to list all candidates of TJLs from the database of dimensions. With the aid of this proposed method, the TJLs have been successfully designed and implemented on a legged robot.

ACS Style

Min-Chan Hwang; Chiou-Jye Huang; Feifei Liu. Application of Support Vector Machine in Designing Theo Jansen Linkages. Applied Sciences 2019, 9, 371 .

AMA Style

Min-Chan Hwang, Chiou-Jye Huang, Feifei Liu. Application of Support Vector Machine in Designing Theo Jansen Linkages. Applied Sciences. 2019; 9 (3):371.

Chicago/Turabian Style

Min-Chan Hwang; Chiou-Jye Huang; Feifei Liu. 2019. "Application of Support Vector Machine in Designing Theo Jansen Linkages." Applied Sciences 9, no. 3: 371.

Journal article
Published: 01 January 2019 in Applied Mechanics and Materials
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With the development of the concept of Industry 4.0, research relating to robots is being paid more and more attention, among which the humanoid robot is a very important research topic. The humanoid robot is a robot with a bipedal mechanism. Due to the physical mechanism, humanoid robots can maneuver more easily in complex terrains, such as going up and down the stairs. However, humanoid robots often fall from imbalance. Whether or not the robot can stand up on its own after a fall is a key research issue. However, the often used method of hand tuning to allow robots to stand on its own is very inefficient. In order to solve the above problems, this paper proposes an automatic learning system based on Particle Swarm Optimization (PSO). This system allows the robot to learn how to achieve the motion of rebalancing after a fall. To allow the robot to have the capability of object recognition, this paper also applies the Convolutional Neural Network (CNN) to let the robot perform image recognition and successfully distinguish between 10 types of objects. The effectiveness and feasibility of the motion learning algorithm and the CNN based image classification for vision system proposed in this paper has been confirmed in the experimental results.

ACS Style

Ssu Ting Lin; Jun Hu; Chia Hung Shih; Chiou Jye Huang; Ping Huan Kuo. The Development of Supervised Motion Learning and Vision System for Humanoid Robot. Applied Mechanics and Materials 2019, 886, 188 -193.

AMA Style

Ssu Ting Lin, Jun Hu, Chia Hung Shih, Chiou Jye Huang, Ping Huan Kuo. The Development of Supervised Motion Learning and Vision System for Humanoid Robot. Applied Mechanics and Materials. 2019; 886 ():188-193.

Chicago/Turabian Style

Ssu Ting Lin; Jun Hu; Chia Hung Shih; Chiou Jye Huang; Ping Huan Kuo. 2019. "The Development of Supervised Motion Learning and Vision System for Humanoid Robot." Applied Mechanics and Materials 886, no. : 188-193.

Journal article
Published: 16 October 2018 in Energies
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To efficiently manage unstable wind power generation, precise short-term wind speed forecasting is critical. To overcome the challenges in wind speed forecasting, this paper proposes a new convolutional neural network algorithm for short-term forecasting. In this paper, the forecasting performance of the proposed algorithm was compared to that of four other artificial intelligence algorithms commonly used in wind speed forecasting. Numerical testing results based on data from a designated wind site in Taiwan were used to demonstrate the efficiency of above-mentioned proposed learning method. Mean absolute error (MAE) and root-mean-square error (RMSE) were adopted as accuracy evaluation indexes in this paper. Experimental results indicate that the MAE and RMSE values of the proposed algorithm are 0.800227 and 0.999978, respectively, demonstrating very high forecasting accuracy.

ACS Style

Chiou-Jye Huang; Ping-Huan Kuo. A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems. Energies 2018, 11, 2777 .

AMA Style

Chiou-Jye Huang, Ping-Huan Kuo. A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems. Energies. 2018; 11 (10):2777.

Chicago/Turabian Style

Chiou-Jye Huang; Ping-Huan Kuo. 2018. "A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems." Energies 11, no. 10: 2777.

Journal article
Published: 10 July 2018 in Sensors
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In modern society, air pollution is an important topic as this pollution exerts a critically bad influence on human health and the environment. Among air pollutants, Particulate Matter (PM2.5) consists of suspended particles with a diameter equal to or less than 2.5 μm. Sources of PM2.5 can be coal-fired power generation, smoke, or dusts. These suspended particles in the air can damage the respiratory and cardiovascular systems of the human body, which may further lead to other diseases such as asthma, lung cancer, or cardiovascular diseases. To monitor and estimate the PM2.5 concentration, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are combined and applied to the PM2.5 forecasting system. To compare the overall performance of each algorithm, four measurement indexes, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) Pearson correlation coefficient and Index of Agreement (IA) are applied to the experiments in this paper. Compared with other machine learning methods, the experimental results showed that the forecasting accuracy of the proposed CNN-LSTM model (APNet) is verified to be the highest in this paper. For the CNN-LSTM model, its feasibility and practicability to forecast the PM2.5 concentration are also verified in this paper. The main contribution of this paper is to develop a deep neural network model that integrates the CNN and LSTM architectures, and through historical data such as cumulated hours of rain, cumulated wind speed and PM2.5 concentration. In the future, this study can also be applied to the prevention and control of PM2.5.

ACS Style

Chiou-Jye Huang; Ping-Huan Kuo. A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities. Sensors 2018, 18, 2220 .

AMA Style

Chiou-Jye Huang, Ping-Huan Kuo. A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities. Sensors. 2018; 18 (7):2220.

Chicago/Turabian Style

Chiou-Jye Huang; Ping-Huan Kuo. 2018. "A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities." Sensors 18, no. 7: 2220.

Journal article
Published: 25 June 2018 in Applied System Innovation
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The primary issue in developing hexapod robots is generating legged motion without tumbling. However, when the hexapod is designed with collocated actuators, where each joint is directly mounted with an actuator, the number of actuators is usually high. The adverse effects of using a great number of actuators include the rise in the challenge of algorithms to control legged motion, the decline in loading capacity, and the increase in the cost of construction. In order to alleviate these problems, we propose a hexapod robot design with non-collocated actuators which is achieved through mechanisms. This hexapod robot is reliable and robust which, because of its mechanism-generated (as opposed to computer-generated) tripod gaits, is always is statically stable, even if running out of battery or due to electronic failure.

ACS Style

Min-Chan Hwang; Chiou-Jye Huang; Feifei Liu. A Hexapod Robot with Non-Collocated Actuators. Applied System Innovation 2018, 1, 20 .

AMA Style

Min-Chan Hwang, Chiou-Jye Huang, Feifei Liu. A Hexapod Robot with Non-Collocated Actuators. Applied System Innovation. 2018; 1 (3):20.

Chicago/Turabian Style

Min-Chan Hwang; Chiou-Jye Huang; Feifei Liu. 2018. "A Hexapod Robot with Non-Collocated Actuators." Applied System Innovation 1, no. 3: 20.

Journal article
Published: 29 May 2018 in Energies
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The power generation potential of a solar photovoltaic (PV) power generation system is closely related to the on-site solar radiation, and sunshine conditions are an important reference index for evaluating the installation of a solar PV system. Meanwhile, the long-term operation and maintenance of a PV system needs solar radiation information as a reference for system performance evaluation. Obtaining solar radiation information through the installation of irradiation monitoring stations is often very costly, and the cost of sustaining the reliability of the monitoring system, Internet stability and subsequent operation and maintenance can often be alarming. Therefore, the establishment of a solar radiation estimation model can reduce the installation of monitoring stations and decrease the cost of obtaining solar radiation information. In this study, we use an inverse distance weighting algorithm to establish the solar radiation estimation model. The model was built by obtaining information from 20 solar radiation monitoring stations in central and southern Taiwan, and field verification was implemented at Yuan Chang Township town hall and the Tainan Liujia campus. Furthermore, a full comparison between Inverse Distance Weighting (IDW) and the Kriging method is also given in this paper. The estimation results demonstrate the performance of the IDW method. In the experiment, the performance of the IDW method is better than the Ordinary Kriging (OK) method. The Mean Absolute Percentage Error (MAPE) values of the solar radiation estimation model by IDW at the two field verifications were 4.30% and 3.71%, respectively.

ACS Style

Ping-Huan Kuo; Hsin-Chuan Chen; Chiou-Jye Huang. Solar Radiation Estimation Algorithm and Field Verification in Taiwan. Energies 2018, 11, 1374 .

AMA Style

Ping-Huan Kuo, Hsin-Chuan Chen, Chiou-Jye Huang. Solar Radiation Estimation Algorithm and Field Verification in Taiwan. Energies. 2018; 11 (6):1374.

Chicago/Turabian Style

Ping-Huan Kuo; Hsin-Chuan Chen; Chiou-Jye Huang. 2018. "Solar Radiation Estimation Algorithm and Field Verification in Taiwan." Energies 11, no. 6: 1374.

Journal article
Published: 21 April 2018 in Sustainability
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Electricity price is a key influencer in the electricity market. Electricity market trades by each participant are based on electricity price. The electricity price adjusted with the change in supply and demand relationship can reflect the real value of electricity in the transaction process. However, for the power generating party, bidding strategy determines the level of profit, and the accurate prediction of electricity price could make it possible to determine a more accurate bidding price. This cannot only reduce transaction risk, but also seize opportunities in the electricity market. In order to effectively estimate electricity price, this paper proposes an electricity price forecasting system based on the combination of 2 deep neural networks, the Convolutional Neural Network (CNN) and the Long Short Term Memory (LSTM). In order to compare the overall performance of each algorithm, the Mean Absolute Error (MAE) and Root-Mean-Square error (RMSE) evaluating measures were applied in the experiments of this paper. Experiment results show that compared with other traditional machine learning methods, the prediction performance of the estimating model proposed in this paper is proven to be the best. By combining the CNN and LSTM models, the feasibility and practicality of electricity price prediction is also confirmed in this paper.

ACS Style

Ping-Huan Kuo; Chiou-Jye Huang. An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks. Sustainability 2018, 10, 1280 .

AMA Style

Ping-Huan Kuo, Chiou-Jye Huang. An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks. Sustainability. 2018; 10 (4):1280.

Chicago/Turabian Style

Ping-Huan Kuo; Chiou-Jye Huang. 2018. "An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks." Sustainability 10, no. 4: 1280.

Journal article
Published: 02 April 2018 in Energies
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The photovoltaic (PV) systems generate green energy from the sunlight without any pollution or noise. The PV systems are simple, convenient to install, and seldom malfunction. Unfortunately, the energy generated by PV systems depends on climatic conditions, location, and system design. The solar radiation forecasting is important to the smooth operation of PV systems. However, solar radiation detected by a pyranometer sensor is strongly nonlinear and highly unstable. The PV energy generation makes a considerable contribution to the smart grids via a large number of relatively small PV systems. In this paper, a high-precision deep convolutional neural network model (SolarNet) is proposed to facilitate the solar radiation forecasting. The proposed model is verified by experiments. The experimental results demonstrate that SolarNet outperforms other benchmark models in forecasting accuracy as well as in predicting complex time series with a high degree of volatility and irregularity.

ACS Style

Ping-Huan Kuo; Chiou-Jye Huang. A Green Energy Application in Energy Management Systems by an Artificial Intelligence-Based Solar Radiation Forecasting Model. Energies 2018, 11, 819 .

AMA Style

Ping-Huan Kuo, Chiou-Jye Huang. A Green Energy Application in Energy Management Systems by an Artificial Intelligence-Based Solar Radiation Forecasting Model. Energies. 2018; 11 (4):819.

Chicago/Turabian Style

Ping-Huan Kuo; Chiou-Jye Huang. 2018. "A Green Energy Application in Energy Management Systems by an Artificial Intelligence-Based Solar Radiation Forecasting Model." Energies 11, no. 4: 819.

Conference paper
Published: 01 April 2018 in 2018 IEEE International Conference on Applied System Invention (ICASI)
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In this study, we use the inverse distance weighting algorithm to establish the solar irradiation estimation model. The model was built by obtaining information from 20 solar irradiation monitoring stations in central and southern Taiwan, and field verification was implemented at Yuan Chang Township town hall and Liujia Campus of Tainan. The Mean Absolute Percentage Error (MAPE) values of the solar irradiation estimation model at the two field verifications were 4.30% and 3.71% respectively.

ACS Style

Ping-Huan Kuo; Hsin-Chuan Chen; Chiou-Jye Huang. Solar daily irradiation estimation algorithm development and field verification. 2018 IEEE International Conference on Applied System Invention (ICASI) 2018, 166 -167.

AMA Style

Ping-Huan Kuo, Hsin-Chuan Chen, Chiou-Jye Huang. Solar daily irradiation estimation algorithm development and field verification. 2018 IEEE International Conference on Applied System Invention (ICASI). 2018; ():166-167.

Chicago/Turabian Style

Ping-Huan Kuo; Hsin-Chuan Chen; Chiou-Jye Huang. 2018. "Solar daily irradiation estimation algorithm development and field verification." 2018 IEEE International Conference on Applied System Invention (ICASI) , no. : 166-167.

Conference paper
Published: 01 April 2018 in 2018 IEEE International Conference on Applied System Invention (ICASI)
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Most of hexapod robots are primarily built to emulate the motion of spiders without loading capacity because the RC servos they used belong to light weight application. Besides, the way of installing each joint with an actuator causes a great number of servos required in general. For engineering interest, the robot has to be able to undertake a load. Notably as serving for salvage, it also has to be controlled wirelessly from a remote site. Hence, we enhanced the loading capacity of the robot by replacing servos with powerful motors and designing an innovative mechanism to lessen the number of actuators. The wireless remote control based on the TCP/IP connection was implemented.

ACS Style

Min-Chan Hang; Chiou-Jye Huang; Feifei Liu. The development of a hexapod robot with wireless remote control. 2018 IEEE International Conference on Applied System Invention (ICASI) 2018, 548 -549.

AMA Style

Min-Chan Hang, Chiou-Jye Huang, Feifei Liu. The development of a hexapod robot with wireless remote control. 2018 IEEE International Conference on Applied System Invention (ICASI). 2018; ():548-549.

Chicago/Turabian Style

Min-Chan Hang; Chiou-Jye Huang; Feifei Liu. 2018. "The development of a hexapod robot with wireless remote control." 2018 IEEE International Conference on Applied System Invention (ICASI) , no. : 548-549.

Journal article
Published: 16 January 2018 in Energies
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One of the most important research topics in smart grid technology is load forecasting, because accuracy of load forecasting highly influences reliability of the smart grid systems. In the past, load forecasting was obtained by traditional analysis techniques such as time series analysis and linear regression. Since the load forecast focuses on aggregated electricity consumption patterns, researchers have recently integrated deep learning approaches with machine learning techniques. In this study, an accurate deep neural network algorithm for short-term load forecasting (STLF) is introduced. The forecasting performance of proposed algorithm is compared with performances of five artificial intelligence algorithms that are commonly used in load forecasting. The Mean Absolute Percentage Error (MAPE) and Cumulative Variation of Root Mean Square Error (CV-RMSE) are used as accuracy evaluation indexes. The experiment results show that MAPE and CV-RMSE of proposed algorithm are 9.77% and 11.66%, respectively, displaying very high forecasting accuracy.

ACS Style

Ping-Huan Kuo; Chiou-Jye Huang. A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting. Energies 2018, 11, 213 .

AMA Style

Ping-Huan Kuo, Chiou-Jye Huang. A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting. Energies. 2018; 11 (1):213.

Chicago/Turabian Style

Ping-Huan Kuo; Chiou-Jye Huang. 2018. "A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting." Energies 11, no. 1: 213.

Conference paper
Published: 01 July 2017 in 2017 International Conference on Machine Learning and Cybernetics (ICMLC)
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This paper proposed the reconfigured unified power flow controller (RUPFC) design with the cascaded H-bridge converter to realize the transferability between the converter branches of shunt and series. An improved recurrent fuzzy neural network (IRFNN) was used to improve the interaction performance of real and reactive power. The flow control for the RUPFC is formed for the series converter. The fast and stable response of dc link voltage and bus voltage of RUPFC control are expected. The proposed control schemes can improve the shortcoming of the conventional power flow controllers, such as small disturbance linearizing methods. Finally, the effectiveness of the proposed scheme is provided to demonstrate in simulation results.

ACS Style

Kai-Hung Lu; Hsin-Chuan Chen; Chiou-Jye Huang; Zhi-Feng Huang. Design of IRFNN for reconfigured UPFC to power fflow control and stability improvement. 2017 International Conference on Machine Learning and Cybernetics (ICMLC) 2017, 2, 436 -443.

AMA Style

Kai-Hung Lu, Hsin-Chuan Chen, Chiou-Jye Huang, Zhi-Feng Huang. Design of IRFNN for reconfigured UPFC to power fflow control and stability improvement. 2017 International Conference on Machine Learning and Cybernetics (ICMLC). 2017; 2 ():436-443.

Chicago/Turabian Style

Kai-Hung Lu; Hsin-Chuan Chen; Chiou-Jye Huang; Zhi-Feng Huang. 2017. "Design of IRFNN for reconfigured UPFC to power fflow control and stability improvement." 2017 International Conference on Machine Learning and Cybernetics (ICMLC) 2, no. : 436-443.

Conference paper
Published: 01 June 2017 in 2017 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW)
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Most of the OBU (On Board Unit) devices of vehicles use MCU to control the infrared transmission and reception for the infrared-oriented parking systems. However, the more battery power of the OBU device will be dissipated. In this paper, a non-processor OBU device is proposed to reduce its standby power consumption. Besides, when the OBU device receives an infrared induced signal to cause transmitting an infrared identification signal, the data collision also can be avoided by the design of the delay control circuit to further increase the stability of identification.

ACS Style

Hsin-Chuan Chen; Chiou-Jye Huang; Kai-Hung Lu. Design of a non-processor OBU device for parking system based on infrared communication. 2017 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW) 2017, 297 -298.

AMA Style

Hsin-Chuan Chen, Chiou-Jye Huang, Kai-Hung Lu. Design of a non-processor OBU device for parking system based on infrared communication. 2017 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW). 2017; ():297-298.

Chicago/Turabian Style

Hsin-Chuan Chen; Chiou-Jye Huang; Kai-Hung Lu. 2017. "Design of a non-processor OBU device for parking system based on infrared communication." 2017 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW) , no. : 297-298.