Short Term Load Forecasting Using ANN and Multiple Linear Regression

Kumar, Sharad; Mishra, Shashank; Gupta, Shashank

doi:10.1109/cict.2016.44

Cited by 36 publications

(12 citation statements)

References 1 publication

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“…Dengan menggunakan metode forecasting regresi linier [16], [17], [18], dalam metode ini, ada data yang nantinya digunakan sebagai bahan untuk membentuk persamaan Y= a+b x [17], berikut ini penjelasannya :…”

Section: Forecasting Methodsunclassified

Tantangan Ekonomi Digital Untuk Pariwisata Indonesia Menggunakan Metode Forecasting

Ismail

Hidayat

2019

teknokom

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Perkembangan ekonomi digital di Indonesia telah mengalami kemajuan yang sangat pesat salah satunya di bidang pariwisata. Kemajuan ekonomi digital dibidang pariwisata dapat menarik wisata nusantara (winus) maupun manca negara berkunjung ketempat wisata Indonesia. Dampak dari kemajuan ekonomi digital mengangkat banyak usaha kecil dan menengah untuk memasuki bisnis dunia. Pertumbuhan ekonomi Indonesia yang cukup tinggi layanan kepariwisataan Indonesia masih sedikit layanan digital yang dikembangkan. Dari analisa perkembangan layanan digital dibidang pariwisata menggunakan method forecasting dari data yang diambil dapat memprediksi 10 tahun ditahun 2028 pengeluaran 465,1 Triliun rupiah dan perjalanan 425,75 juta kali perjalanan kalau dibandingkan dari tahun 2018 pada pengeluaran 291,09 Trilium rupiah dan perjalanan hanya 303,4 juta kali perjalanan, dari analisa yang didapat memiliki peluang bagi teknopreneur untuk membuat starup dibidang pariwisata yang menjembatani atara pariwiswata dan wisatawan nusantara tentu menguntungkan bagi pertumbuhan ekonomi Indonesia dengan menggunakan digital ekonomi di era industri ekonomi digital ini.

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Section: Forecasting Methodsunclassified

Tantangan Ekonomi Digital Untuk Pariwisata Indonesia Menggunakan Metode Forecasting

Ismail

Hidayat

2019

teknokom

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“…Traditional forecasting has been thoroughly investigated and widely applied because of its high computational speed, robustness, and ease of implementation [10]. Machine learning methods and their expansions have been the subject of research interest for several decades; they include linear regression [11,12], multiple linear regression [13,14], and KNN [15]. For applications based on linear forecasting, these models are an excellent choice as they reflect the relationships among the features of output load and relevant factors.…”

Section: Related Workmentioning

confidence: 99%

Multi-Behavior with Bottleneck Features LSTM for Load Forecasting in Building Energy Management System

Nguyen

et al. 2021

Electronics

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With the wide use of the Internet of Things and artificial intelligence, energy management systems play an increasingly important role in the management and control of energy consumption in modern buildings. Load forecasting for building energy management systems is one of the most challenging forecasting tasks as it requires high accuracy and stable operating conditions. In this study, we propose a novel multi-behavior with bottleneck features long short-term memory (LSTM) model that combines the predictive behavior of long-term, short-term, and weekly feature models by using the bottleneck feature technique for building energy management systems. The proposed model, along with the unique scheme, provides predictions with the accuracy of long-term memory, adapts to unexpected and unpatternizable intrinsic temporal factors through the short-term memory, and remains stable because of the weekly features of input data. To verify the accuracy and stability of the proposed model, we present and analyze several learning models and metrics for evaluation. Corresponding experiments are conducted and detailed information on data preparation and model training are provided. Relative to single-model LSTM, the proposed model achieves improved performance and displays an excellent capability to respond to unexpected situations in building energy management systems.

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“…It has many features that make it attractive for problems such as pricing options, with the capability of developing nonlinear model relationships that do not depend on the restrictive assumptions implied in the parametric approach, or on the specification of the theory that connects the prices of underlying assets to the prices of options. The implementation of an ANN model is considered successful when it has the ability to learn from the provided data and use the data in a new way [44][45][46][47][48].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus

Solyalı

2020

Sustainability

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Estimating the electricity load is a crucial task in the planning of power generation systems and the efficient operation and sustainable growth of modern electricity supply networks. Especially with the advent of smart grids, the need for fairly precise and highly reliable estimation of electricity load is greater than ever. It is a challenging task to estimate the electricity load with high precision. Many energy demand management methods are used to estimate future energy demands correctly. Machine learning methods are well adapted to the nature of the electrical load, as they can model complicated nonlinear connections through a learning process containing historical data patterns. Many scientists have used machine learning (ML) to anticipate failure before it occurs as well as predict the outcome. ML is an artificial intelligence (AI) subdomain that involves studying and developing mathematical algorithms to understand data or obtain data directly without relying on a prearranged model algorithm. ML is applied in all industries. In this paper, machine learning strategies including artificial neural network (ANN), multiple linear regression (MLR), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) were used to estimate electricity demand and propose criteria for power generation in Cyprus. The simulations were adapted to real historical data explaining the electricity usage in 2016 and 2107 with long-term and short-term analysis. It was observed that electricity load is a result of temperature, humidity, solar irradiation, population, gross national income (GNI) per capita, and the electricity price per kilowatt-hour, which provide input parameters for the ML algorithms. Using electricity load data from Cyprus, the performance of the ML algorithms was thoroughly evaluated. The results of long-term and short-term studies show that SVM and ANN are comparatively superior to other ML methods, providing more reliable and precise outcomes in terms of fewer estimation errors for Cyprus’s time series forecasting criteria for power generation.

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Short Term Load Forecasting Using ANN and Multiple Linear Regression

Cited by 36 publications

References 1 publication

Tantangan Ekonomi Digital Untuk Pariwisata Indonesia Menggunakan Metode Forecasting

Tantangan Ekonomi Digital Untuk Pariwisata Indonesia Menggunakan Metode Forecasting

Multi-Behavior with Bottleneck Features LSTM for Load Forecasting in Building Energy Management System

A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus

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