Rule Optimization of Fuzzy Inference System Sugeno using Evolution Strategy for Electricity Consumption Forecasting

Santika, Gayatri Dwi; Mahmudy, Wayan Firdaus; Naba, Agus

doi:10.11591/ijece.v7i4.pp2241-2252

Cited by 5 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Parameter ANFIS dapat dipisahkan menjadi dua, yaitu parameter premis dan konsekuen yang dapat diadaptasikan dengan pelatihan hybrid. Pelatihan hybrid dilakukan dalam dua langkah, yaitu langkah maju dan langkah mundur (Santika et al, 2017). System (ANFIS) yang digunakan dalam penelitian ini adalah metode yang menggabungkan kecerdasan tiruan dari jaringan saraf (neural network) dengan kecemerlangan inferensi fuzzy untuk memodelkan hubungan antara input dan output dengan fuzzy base sugeno, fuzzy rule base, dan jaringan syaraf tiruan (JST).…”

Section: Metode Analisis Neuro-fuzzy Anfisunclassified

Pengintegrasian Teknologi Internet of Things dalam Optimalisasi Pemupukan Organik untuk Pertumbuhan dan Hasil Panen Sacha Inchi

Andayani,

Wijaya,

Prasetyo

et al. 2024

Agrikultura

View full text Add to dashboard Cite

Tanaman sacha inchi memiliki potensi ekonomi yang baik sehingga perlu pengembangan dengan mempertimbangkan sistem produksi berkelanjutan. Salah satu pendukung peningkatan pertumbuhan dan hasil panen adalah pemupukan dengan bahan organik. Tujuan dari penelitian ini adalah menganalisis berbagai perlakuan pupuk dari bahan organik dari kotoran hewan ayam, kambing, dan sapi terhadap pertumbuhan dan hasil panen sehingga terbentuk model yang optimal dari berbagai perlakuan jenis pupuk dengan memanfaatkan teknologi Internet of Things (IoT) dalam penyiraman dan pemupukan otomatis. Model perlakuan pupuk yang optimal dikembangkan dengan menggunakan metode ANFIS. Pengumpulan data di mulai bulan Maret hingga September 2023 di Desa Cikadu. Kecamatan Sindangkerta, Kabupaten Bandung Barat Provinsi Jawa Barat. Hasil penelitian menunjukkan bahwa aplikasi perlakuan pupuk organik dengan bahan kotoran hewan sapi menunjukkan hasil yang optimal pada paremeter pertumbuhan jumlah daun, jumlah batang dan jumlah buah potensial dengan akurasi prediksi terhadap model yang dihasilkan sebesar 96%. Implikasi hasil penelitian ini secara praktek dapat memanfaatkan bahan organik kotoran sapi sebagai alternatif pemupukan yang selama ini belum dimanfaatkan dan dijadikan nilai tambah. Implikasi lain secara teori yaitu dapat mengembangkan keberlanjutan produksi melalui pertumbuahn jumlah daun, jumlah batang dan jumlah buah potensial yang berdampak pada hasil panen sehingga ekosistem dalam mendukung agroindustri sacha inchi dapat tetap terjaga.

show abstract

Section: Metode Analisis Neuro-fuzzy Anfisunclassified

Pengintegrasian Teknologi Internet of Things dalam Optimalisasi Pemupukan Organik untuk Pertumbuhan dan Hasil Panen Sacha Inchi

Andayani,

Wijaya,

Prasetyo

et al. 2024

Agrikultura

View full text Add to dashboard Cite

show abstract

“…This algorithm employs fuzzy sets to handle uncertainties and can generate an inputoutput data mapping based on human knowledge [28]. ANFIS has demonstrated remarkable results in various fields such as medical diagnosis, forecasting energy demand, and system performance [29][30][31][32][33][34]. The ANFIS applications above are only used for system modeling, parameter optimization, and forecasting but the existing literature mentioned the implementation of ANFIS on real systems remains limited.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Adaptive Network-Based Fuzzy Inference for Hybrid Ground Source Heat Pump

Chuensiri,

Katchasuwanmanee,

Wisessint

et al. 2024

IEEE Access

View full text Add to dashboard Cite

This paper presents a new development of an Adaptive Network-based Fuzzy Inference System (ANFIS) for a Hybrid Ground Source Heat Pump (HGSHP). The HGSHP is equipped with a supplementary heat sink composter to process organic solid waste (OSW), utilizing excess hot air from the condensing unit to aerate the compost pile. The Fuzzy Logic Controller (FLC) was developed using data collected by effective sensors installed in the HGSHP system. The main objective is to control the water flow rate with a Variable Speed Drive (VSD) to improve overall system performance. The dataset for ANFIS has been created and trained using MATLAB® software, then implemented on a Raspberry Pi nano-computer with Python coding. This paper compares the performance of ANFIS with two different cases: ANFIS with Triangular Membership Function (TriMF) and ANFIS with Gaussian Membership Function (GaussMF). After implementing ANFIS with TriMF and GaussMF, the average COP during composter operation and system cooling significantly increased. In contrast, the HGSHP system power consumption is sufficiently reduced in both case studies. Moreover, ANFIS also benefits the composting process, as evidenced by the increase in composter operation time, and vice versa for system cooling time. Ultimately, the implementation of ANFIS can improve the HGSHP system performance in both the TriMF and GaussMF cases, with the TriMF case showing a significant improvement in the HGSHP system performance compared to the GaussMF case.

show abstract

“…The third type is short-term load forecasting (STLF). STLF mainly covers the period of one week, and refers to the assessment of load per hour during the day [5]. This type of prediction is more specific in time as it considers hourly prediction.…”

Section: Introductionmentioning

confidence: 99%

An Accurate Medium-Term Load Forecasting based on Hybrid Technique

Yasin¹,

Aziz²,

Salim³

et al. 2018

IJEECS

View full text Add to dashboard Cite

An accurate medium term load forecasting is significant for power generation scheduling, economic and reliable operation in power system. Most of classical approach for medium term load forecasting only consider total daily load demand. This approach may not provide accurate results since the load demand is fluctuated in a day. In this paper, a hybrid Ant-Lion Optimizer Least-square Support Vector Machine (ALO-LSSVM) is proposed to forecast 24-hour load demand for the next year. Ant-Lion Optimizer (ALO) is utilized to optimize the RBF Kernel parameters in Least-Square Support Vector Machine (LS-SVM). The objective of the optimization is to minimize the Mean Absolute Percentage Error (MAPE). The performance of ALO-LSSVM technique was compared with those obtained from LS-SVM technique through a 10-fold cross-validation procedure. The historical hourly load data are analyzed and appropriate features are selected for the model. There are 24 inputs and 24 outputs vectors for this model which represents 24-hour load demand for whole year. The results revealed that the high accuracy of prediction could be achieved using ALO-LSSVM.

show abstract

Rule Optimization of Fuzzy Inference System Sugeno using Evolution Strategy for Electricity Consumption Forecasting

Cited by 5 publications

References 25 publications

Pengintegrasian Teknologi Internet of Things dalam Optimalisasi Pemupukan Organik untuk Pertumbuhan dan Hasil Panen Sacha Inchi

Pengintegrasian Teknologi Internet of Things dalam Optimalisasi Pemupukan Organik untuk Pertumbuhan dan Hasil Panen Sacha Inchi

Implementation of Adaptive Network-Based Fuzzy Inference for Hybrid Ground Source Heat Pump

An Accurate Medium-Term Load Forecasting based on Hybrid Technique

Contact Info

Product

Resources

About