Design of a multi-level fuzzy linear regression model for forecasting transport energy demand: A case study of Iran

Taghizadeh, M. R.; Shakouri, H.; Menhaj, Mohammad Bagher; Mehregan, Mohammad Reza; Kazemi, Aliyeh

doi:10.1109/iccie.2009.5223766

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…The constraints require that each observation 𝑦 𝑖 (𝑜𝑟 𝑦 𝑡 in the case of FARIMA) has a threshold value ℎ in the interval (0, 1) which is specified by the user of belonging to 𝑦 ̃(𝑦) (Taghizadeh et al 2011). This implies, 𝑦 ̃(𝑦 𝑖 ) ≥ ℎ, 𝑖 = 1, 2, .…”

Section: Constraintsmentioning

confidence: 99%

Comparative analysis of Fuzzy Regression and Fuzzy ARIMA models for forecasting real gross domestic product of Nigeria

Bakawu,

Buba,

Malik

2023

dujopas

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Over the years, a number of studies have been conducted in terms of forecasting the real gross domestic product (GDP) of Nigeria. The GDP growth rate measures the annual growth rate in percentage of the monetary value of all finished goods and services made within a country. It is an important indicator of economic growth. This paper presents Fuzzy ARIMA and Regression to determine the interval of possibility for predicting the real GDP. The Fuzzy ARIMA (FARIMA) and Fuzzy Regression (FR) methods requires small size data as compared to the classical time series. Comparison between FARIMA and Fuzzy Regression with a threshold level of zero (ℎ=0) is performed by calibrating the models on existing data. The minimum values of the total spreads for FARIMA and FR are 0.791 and 4.077 respectively. In addition, the MAPE values for FARIMA is smaller than that of FR. Furthermore, the results indicate that the FARIMA gives a narrower interval of possibility for prediction than the FR.

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Section: Constraintsmentioning

confidence: 99%

Comparative analysis of Fuzzy Regression and Fuzzy ARIMA models for forecasting real gross domestic product of Nigeria

Bakawu,

Buba,

Malik

2023

dujopas

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“…A different plan was analysed based on the 'Partial-least square regression (PLSR)' technique for estimating electricity consumption in Iran. Also, authors stated that the energy consumption in Iran is gradually reaching its maximum day by day [53]. A. Satre et al estimated modelling of annual energy consumption in buildings.…”

Section: 73mentioning

confidence: 99%

A review on short‐term load forecasting models for micro‐grid application

Kondaiah

Saravanan

Sanjeevikumar

et al. 2022

The Journal of Engineering

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Load forecasting (LF), particularly short‐term load forecasting (STLF), plays a vital role throughout the operation of the conventional power system. The precise modelling and complex analyses of STLF have become more significant in advanced microgrid (MG) applications. Several models are proposed for STLF and tested successfully in the literature. The selection of a forecasting method is mostly based on data availability and its objectives. This article presents a survey of the latest analytical and approximation techniques reported in the literature to model STLF in an MG environment. This article mainly focusses on the review on important methods applied to forecast renewable energy availability, energy demand, and price and load demand. Different models, their main objectives, methodology, error percentage, and so forth, are critically reviewed and analysed. For quick reference, we have highlighted the important points in the form tables. The researchers can quickly identify and frame their research problem related to the LF area by reading this review paper.

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“…Demand forecasting [33,34] in the supply chain is of great signifcance to companies and users. Based on the results of the forecast, companies can adjust production capacity (such as seasonal labor), subcontract, build inventory, and postpone delivery to achieve supply management through short-term price discounts and promotion to achieve demand management.…”

Section: Background and Related Workmentioning

confidence: 99%

Federated Learning for Supply Chain Demand Forecasting

Wang

Xie

Duan

et al. 2022

Mathematical Problems in Engineering

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With the country’s policy support and the rapid development of Internet technology, the domestic consumption level has been escalating and the consumption structure has changed. The traditional retail industry cannot integrate all the relevant data due to data security and privacy protection concerns so that it is unable to adjust sales strategies in an accurate and timely manner. New retail has sounded the clarion call for the retail revolution. The supply chain demand forecasting is an important problem for the supply chain management. In this research, we propose a new retail supply chain commodity demand forecasting framework based on vertical federal learning, which solves the problems of data security and privacy faced by new retail theoretically and empirically. In experiments, we use datasets from different platforms (such as social platforms, e-commerce platforms, and retailers) in the same region for federated learning. The experiment results demonstrate the superiority of the proposed algorithm.

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Design of a multi-level fuzzy linear regression model for forecasting transport energy demand: A case study of Iran

Cited by 7 publications

References 13 publications

Comparative analysis of Fuzzy Regression and Fuzzy ARIMA models for forecasting real gross domestic product of Nigeria

Comparative analysis of Fuzzy Regression and Fuzzy ARIMA models for forecasting real gross domestic product of Nigeria

A review on short‐term load forecasting models for micro‐grid application

Federated Learning for Supply Chain Demand Forecasting

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