scholarly journals Seasonal Based Electricity Demand Forecasting Using Time Series Analysis

2016 ◽  
Vol 07 (10) ◽  
pp. 3320-3328 ◽  
Author(s):  
T. M. Usha ◽  
S. Appavu Alias Balamurugan
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Demand Forecasting ◽  
Electricity Demand ◽  
Series Analysis ◽  
Electricity Demand Forecasting

Analysis of Time Series Models for Brazilian Electricity Demand Forecasting

2021 ◽  
Author(s):  
Carlos Eduardo Velasquez Cabrera ◽  
Matheus Zocatelli ◽  
Fidellis B.G.L. e Estanislau ◽  
Victor Faria
Keyword(s):  
Time Series ◽  
Demand Forecasting ◽  
Electricity Demand ◽  
Time Series Models ◽  
Electricity Demand Forecasting ◽  
Analysis Of Time Series

scholarly journals Electricity demand for Sri Lanka: A time series analysis

Energy ◽  
2008 ◽  
Vol 33 (5) ◽  
pp. 724-739 ◽  
Author(s):  
H AMARAWICKRAMA ◽  
L HUNT
Keyword(s):  
Time Series ◽  
Sri Lanka ◽  
Time Series Analysis ◽  
Electricity Demand ◽  
Series Analysis

Demand Forecasting in Hybrid MTS/MTO Production Systems

2018 ◽  
Vol 5 (1) ◽  
pp. 63-78
Author(s):  
Moeen Sammak Jalali ◽  
S.M.T. Fatemi Ghomi
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Manufacturing Systems ◽  
Production Efficiency ◽  
Production Systems ◽  
Demand Forecasting ◽  
Series Analysis ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Manufacturing Environments

This article describes how simplifying production-planning approaches for demand responsiveness has been well recognized as an operative means of accomplishing production efficiency. To support an effective decision making in manufacturing environments, this study will focus on adopting time series analysis concepts. It will attempt to focus on bringing forward novel structures for classifications of available surveying materials, which helps companies using time series analysis within production strategies to make a logical prediction of demands in hybrid manufacturing systems. In this regard, the authors will present two different categorizing structures as efficient ways of helping practitioners and academicians to find new approaches for applying near possible future forecasts by means of time series analysis methods.

scholarly journals Estimation of electricity demand function for Algeria: Revisit of time series analysis

2018 ◽  
Vol 82 ◽  
pp. 4221-4234 ◽  
Author(s):  
Naceur Khraief ◽  
Muhammad Shahbaz ◽  
Hrushikesh Mallick ◽  
Nanthakumar Loganathan
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Demand Function ◽  
Electricity Demand ◽  
Series Analysis

scholarly journals An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Zeynep Hilal Kilimci ◽  
A. Okay Akyuz ◽  
Mitat Uysal ◽  
Selim Akyokus ◽  
M. Ozan Uysal ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Support Vector Regression ◽  
Historical Data ◽  
Demand Forecasting ◽  
Support Vector ◽  
Series Analysis ◽  
Integration Strategy ◽  
Forecasting System

Demand forecasting is one of the main issues of supply chains. It aimed to optimize stocks, reduce costs, and increase sales, profit, and customer loyalty. For this purpose, historical data can be analyzed to improve demand forecasting by using various methods like machine learning techniques, time series analysis, and deep learning models. In this work, an intelligent demand forecasting system is developed. This improved model is based on the analysis and interpretation of the historical data by using different forecasting methods which include time series analysis techniques, support vector regression algorithm, and deep learning models. To the best of our knowledge, this is the first study to blend the deep learning methodology, support vector regression algorithm, and different time series analysis models by a novel decision integration strategy for demand forecasting approach. The other novelty of this work is the adaptation of boosting ensemble strategy to demand forecasting system by implementing a novel decision integration model. The developed system is applied and tested on real life data obtained from SOK Market in Turkey which operates as a fast-growing company with 6700 stores, 1500 products, and 23 distribution centers. A wide range of comparative and extensive experiments demonstrate that the proposed demand forecasting system exhibits noteworthy results compared to the state-of-art studies. Unlike the state-of-art studies, inclusion of support vector regression, deep learning model, and a novel integration strategy to the proposed forecasting system ensures significant accuracy improvement.

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