Daily Peak Load Forecast of Banda Aceh City Using Adaptive Neuro Fuzzy Inference System (ANFIS) Method

Tindra, Heldi; Sara, Ira Devi; Adriman, Ramzi

doi:10.1109/cosite60233.2023.10249581

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Numerous researchers have dedicated efforts to developing precise forecast models within the power sector, primarily concentrating on predicting demand at the utility scale [18,19]. For example, Tindra et al, in their work in [18], specifically created a peak loadforecast model tailored for Banda Aceh City to address the escalating need for electrical energy. This model, leveraging the Adaptive Neuro-Fuzzy Inference System, incorporates variables, such as the temperature, humidity, and electrical load during peak hours.…”

Section: Related Workmentioning

confidence: 99%

End-to-End Top-Down Load Forecasting Model for Residential Consumers

Parkash,

Lie,

et al. 2024

Energies

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This study presents an efficient end-to-end (E2E) learning approach for the short-term load forecasting of hierarchically structured residential consumers based on the principles of a top-down (TD) approach. This technique employs a neural network for predicting load at lower hierarchical levels based on the aggregated one at the top. A simulation is carried out with 9 (from 2013 to 2021) years of energy consumption data of 50 houses located in the United States of America. Simulation results demonstrate that the E2E model, which uses a single model for different nodes and is based on the principles of a top-down approach, shows huge potential for improving forecasting accuracy, making it a valuable tool for grid planners. Model inputs are derived from the aggregated residential category and the specific cluster targeted for forecasting. The proposed model can accurately forecast any residential consumption cluster without requiring any hyperparameter adjustments. According to the experimental analysis, the E2E model outperformed a two-stage methodology and a benchmarked Seasonal Autoregressive Integrated Moving Average (SARIMA) and Support Vector Regression (SVR) model by a mean absolute percentage error (MAPE) of 2.27%.

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Section: Related Workmentioning

confidence: 99%

End-to-End Top-Down Load Forecasting Model for Residential Consumers

Parkash,

Lie,

et al. 2024

Energies

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“…Teknologi akan sangat membantu dalam proses pengolahan data yang banyak. Oleh karena itu penulis hendak mendalami persoalan ini sebagai bahasan Penelitian [8].…”

Section: Pendahuluanunclassified

Rancang Bangun Aplikasi Penerima Beasiswa Dengan Menggunakan Metode Adaptive Neuro-Fuzzy Interference System (ANFIS)

Siregar,

Raynaldi

2024

JTSI

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Di Perguruan Tinggi, beasiswa biasanya diberikan atas berbagai pertimbangan. Pada umumnya, yang menjadi bahan pertimbangan utama adalah penghasilan orangtua, Indeks Prestasi Kumulatif (IPK) mahasiswa, serta jumlah tanggungan orangtua. Yang menjadi persoalan dalam pemberian beasiswa adalah tak adanya standar pasti untuk menentukan siapa yang berhak memperoleh beasiswa. Penulis membangun aplikasi penentu perolehan beasiswa dengan menggunakan metode Adaptive Neuro-Fuzzy Interference System (ANFIS). Metode ini menggabungkan kemampuan adaptasi yang dimiliki oleh Jaringan Syaraf Tiruan (JST) dengan kemampuan penentuan keputusan secara kualitatif yang dimiliki oleh fuzzy. Dengan aplikasi/program yang menggunakan metode ini maka penentuan perolehan beasiswa dapat dilakukan secara otomatis dengan mempelajari pola-pola sebelumnya.Dari hasil penelitian ini dihasilkan bahwa ANFIS berhasil menganalisa masukan berupa IPK, tanggungan orangtua, dan penghasilan orangtua, lalu menghasikan keluaran berupa nilai hasil analisa ANFIS. Nilai ini kemudian dimanfaatkan untuk memilih penerima beasiswa berdasarkan kuota beasiswa yang disediakan. Dalam penelitian ini penulis menggunakan rule sebanyak 27 rule, dalam arisetektur ANFIS pada aplikasi penerima beasiswa.

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