scholarly journals Support Vector Machine and Long Short-term Memory using Multivariate Models for Wind Power Forecasting

2020 ◽  
Vol 9 (4) ◽  
pp. 2364-2369
Keyword(s):  
Artificial Intelligence ◽  
Support Vector Machine ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Short Term Memory ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Renewable energy has recently gained considerable attention. In particular, interest in wind energy is rapidly increasing globally. However, the characteristics of instability and volatility in wind energy systems also have a significant on power systems. To address these issues, numerous studies have been carried out to predict wind speed and power. Methods used to forecast wind energy are divided into three categories: physical, data-driven (statistical and artificial intelligence methods), and hybrid methods. In this study, among artificial intelligence methods, we compare short-term wind power using a support vector machine (SVM) and long short-term memory (LSTM). The method using an SVM is a short-term wind power forecast that considers the wind speed and direction on Jeju Island, whereas the method using LSTM does not consider the wind speed and direction. As the experiment results indicate, the SVM method achieves an excellent performance when considering the wind speed and direction.

scholarly journals Hybrid Forecasting Model for Short-Term Wind Power Prediction Using Modified Long Short-Term Memory

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3901 ◽  
Author(s):  
Namrye Son ◽  
Seunghak Yang ◽  
Jeongseung Na
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Short Term Memory ◽  
Hybrid Methods ◽  
Observation Data ◽  
Short Term ◽  
Term Memory ◽  
Current Observation ◽  
Long Short Term Memory

Renewable energy has recently gained considerable attention. In particular, the interest in wind energy is rapidly growing globally. However, the characteristics of instability and volatility in wind energy systems also affect power systems significantly. To address these issues, many studies have been carried out to predict wind speed and power. Methods of predicting wind energy are divided into four categories: physical methods, statistical methods, artificial intelligence methods, and hybrid methods. In this study, we proposed a hybrid model using modified LSTM (Long short-term Memory) to predict short-term wind power. The data adopted by modified LSTM use the current observation data (wind power, wind direction, and wind speed) rather than previous data, which are prediction factors of wind power. The performance of modified LSTM was compared among four multivariate models, which are derived from combining the current observation data. Among multivariable models, the proposed hybrid method showed good performance in the initial stage with Model 1 (wind power) and excellent performance in the middle to late stages with Model 3 (wind power, wind speed) in the estimation of short-term wind power. The experiment results showed that the proposed model is more robust and accurate in forecasting short-term wind power than the other models.

Categorizing Natural Language-Based Customer Satisfaction: An Implementation Method Using Support Vector Machine and Long Short-Term Memory Neural Network

2021 ◽  
Vol 13 (4) ◽  
Author(s):  
Ralph Sherwin A. Corpuz ◽  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Natural Language ◽  
Text Categorization ◽  
Short Term Memory ◽  
Support Vector ◽  
Feature Engineering ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Analyzing natural language-based Customer Satisfaction (CS) is a tedious process. This issue is practically true if one is to manually categorize large datasets. Fortunately, the advent of supervised machine learning techniques has paved the way toward the design of efficient categorization systems used for CS. This paper presents the feasibility of designing a text categorization model using two popular and robust algorithms – the Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) Neural Network, in order to automatically categorize complaints, suggestions, feedbacks, and commendations. The study found that, in terms of training accuracy, SVM has best rating of 98.63% while LSTM has best rating of 99.32%. Such results mean that both SVM and LSTM algorithms are at par with each other in terms of training accuracy, but SVM is significantly faster than LSTM by approximately 35.47s. The training performance results of both algorithms are attributed on the limitations of the dataset size, high-dimensionality of both English and Tagalog languages, and applicability of the feature engineering techniques used. Interestingly, based on the results of actual implementation, both algorithms are found to be 100% effective in accurately predicting the correct CS categories. Hence, the extent of preference between the two algorithms boils down on the available dataset and the skill in optimizing these algorithms through feature engineering techniques and in implementing them toward actual text categorization applications.

scholarly journals Analysis of Public Opinion Sentiment on Instagram regarding Covid-19 with SVM

2021 ◽  
Vol 1 (1) ◽  
pp. 67
Author(s):  
Iin Kurniasari ◽  
Kusrini Kusrini ◽  
Hanif Al Fatta
Keyword(s):  
Support Vector Machine ◽  
Public Opinion ◽  
Short Term Memory ◽  
Support Vector ◽  
Response Ratio ◽  
Short Term ◽  
Term Memory ◽  
Social Distancing ◽  
Word Level ◽  
Long Short Term Memory

Perkembangan teknologi dewasa ini mendorong masyarakat untuk selalu tanggap teknologi, terlebih di era pandemi covid-19 yang selalu mengedepankan social distancing. Media sosial digunakan sebagai suatu alat untuk menyampaikan opini masyarakat kepada khalayak. Dalam penelitian ini, penulis melakukan penelitian tentang opini masyaraat pada media sosial instagram dengan mengguakan Support Vector Machine. Setelah dilakukan uji akurasi dan presisi ternyata SVM belum sesuai digunakan sebagai algoritma yang dapat menangkap urutan karena susunan kata yang dibolak-balik meskipun maknanya berbeda tetap bermakna sama oleh mesin SVM, hal ini dibuktikan juga dengan jumlah akurasi yang kecil.yaitu 59%. Sehingga diperlukan langkah untuk bisa diteliti dengan algoritma lain misalnya algoritma HRRN (Highest Response Ratio Next) atau LSTM (Long Short-Term Memory) yang memperhatikan urutan dan proses dengan rasio respon paling tinggi. Jika berdasarkan pendekatan ekstraksi fitur SVM dengan pendekatan count vector, tf-idf word level, tf-idf ngram level dan tf-idf char level. Dalam skenario ini nilai akurasi tertinggi terdapat pada perhitungan dengan menggunakan ekstraksi fitur count vector dan tf-idf ngram level.

Kajian Penggunaan Data Log Mahasiswa untuk Berbagai Permasalahan Analisis Pembelajaran

2020 ◽  
Vol 9 (4) ◽  
pp. 365-374
Author(s):  
Sri Suning Kusumawardani ◽  
Syukron Abu Ishaq Alfarozi
Keyword(s):  
Support Vector Machine ◽  
Learning Analytics ◽  
Short Term Memory ◽  
Drop Out ◽  
Support Vector ◽  
Multi Layer Perceptron ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Data Log

Pada saat ini, penyelenggaraan sistem pembelajaran daring menjadi hal yang penting di tengah pandemi untuk menekan persebaran virus COVID-19. Namun, sistem ini sangat sulit menjaga motivasi dan tingkat keterlibatan mahasiswa karena tidak ada interaksi langsung antara pengajar dengan mahasiswa. Makalah ini meninjau penggunaan data log mahasiswa untuk kebutuhan analisis pembelajaran guna memprediksi kinerja atau kecenderungan drop-out mahasiswa dari suatu mata kuliah dengan melihat pada data log interaksi mahasiswa dengan sistem dan data demografis mahasiswa menggunakan suatu data terbuka, yaitu Open University Learning Analytics Dataset (OULAD). Dari tinjauan beberapa artikel penelitian yang merujuk pada dataset tersebut, ada beberapa hal yang perlu ditinjau: 1) permasalahan yang sering diangkat, yaitu prediksi kecenderungan gagal dari mata kuliah tertentu, prediksi kinerja, dan prediksi keterlibatan mahasiswa; 2) fitur yang digunakan pada saat pemodelan, yaitu fitur demografis dan interaksi, baik yang diringkas secara harian atau mingguan dengan berbagai representasi fitur; 3) metode analisis pembelajaran yang secara khusus menggunakan metode pembelajaran mesin yang sering digunakan, yaitu Decision Tree (DT), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), dan Long Short-Term Memory (LSTM). Makalah ini juga mendiskusikan proses mitigasi dari mahasiswa yang berisiko, perancangan sistem data yang mendukung analisis pembelajaran, dan permasalahan yang sering ditemui pada saat proses pemodelan.

scholarly journals Ultra-Short-Term Prediction of Wind Power Based on Error Following Forget Gate-Based Long Short-Term Memory

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5400
Author(s):  
Pei Zhang ◽  
Chunping Li ◽  
Chunhua Peng ◽  
Jiangang Tian
Keyword(s):  
Wind Power ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Support Vector ◽  
Power Prediction ◽  
Short Term ◽  
Term Memory ◽  
Wind Power Prediction ◽  
Long Short Term Memory ◽  
The Impact

To improve the accuracy of ultra-short-term wind power prediction, this paper proposed a model using modified long short-term memory (LSTM) to predict ultra-short-term wind power. Because the forget gate of standard LSTM cannot reflect the correction effect of prediction errors on model prediction in ultra-short-term, this paper develops the error following forget gate (EFFG)-based LSTM model for ultra-short-term wind power prediction. The proposed EFFG-based LSTM model updates the output of the forget gate using the difference between the predicted value and the actual value, thereby reducing the impact of the prediction error at the previous moment on the prediction accuracy of wind power at this time, and improving the rolling prediction accuracy of wind power. A case study is performed using historical wind power data and numerical prediction meteorological data of an actual wind farm. Study results indicate that the root mean square error of the wind power prediction model based on EFFG-based LSTM is less than 3%, while the accuracy rate and qualified rate are more than 90%. The EFFG-based LSTM model provides better performance than the support vector machine (SVM) and standard LSTM model.

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