Long term electricity demand forecasting using autoregressive integrated moving average model: Case study of Morocco

2015 ◽  
Author(s):  
Noreddine Citroen ◽  
Mohammed Ouassaid ◽  
Mohamed Maaroufi
Keyword(s):  
Moving Average ◽  
Demand Forecasting ◽  
Electricity Demand ◽  
Average Model ◽  
Model Case ◽  
Autoregressive Integrated Moving Average ◽  
Electricity Demand Forecasting ◽  
Moving Average Model

scholarly journals Forecasting monthly farm tractor demand for India using MSARIMA and ARMAX models

2019 ◽  
Author(s):  
Alok Yadav ◽  
Sajal Ghosh
Keyword(s):  
Moving Average ◽  
Demand Forecasting ◽  
Raw Material ◽  
Autoregressive Moving Average ◽  
Average Model ◽  
Autoregressive Integrated Moving Average ◽  
Autoregressive Moving Average Model ◽  
Farm Tractor ◽  
The World ◽  
Moving Average Model

Because of long product development cycles, effective production planning of automobiles requires accurate demand forecasting in order to effectively managing resources and maximizing revenue. Errors in demand forecasts have often led to enormous costs and loss of revenue due to suboptimal utilization of resources. Since early 2000 India has been the largest manufacturer and consumer of farm tractors in the world. This paper develops multiplicative seasonal autoregressive integrated moving average (MSARIMA) and autoregressive moving average model with exogenous variable (ARMAX) to forecast monthly demand for farm tractor. The result indicates that ARMAX with real agriculture credit has found to be outperformed MSARIMA model in forecasting demand of farm tractors in the horizon of six months. The accurate monthly forecasting of farm tractor would help the manufacturers for better raw material, inventory and supply chain management. Keywords

scholarly journals Application of LEAP model on long-term electricity demand forecasting in Indonesia, period 2010-2025

2018 ◽  
Vol 49 ◽  
pp. 02007 ◽  
Author(s):  
Jaka Windarta ◽  
Bambang Purwanggono ◽  
Fuad Hidayanto
Keyword(s):  
Demand Forecasting ◽  
Electricity Consumption ◽  
Electricity Demand ◽  
Planning System ◽  
Large Country ◽  
Electricity Demand Forecasting ◽  
Electricity Planning ◽  
Business As Usual

Electricity demand forecasting is an important part in energy management especially in electricity planning. Indonesia is a large country with a pattern of electricity consumption which continues to increase, therefor need to forecasting electricity demand in order to avoid unbalance demand and supply or deficit energy. LEAP (Long-range Energy Alternative Planning System) as a tool energy model and Indonesia as a case study. Basically, electricity demand is influenced by population, economy and electricity intensity. The purpose of this study is to provide understanding and application of electricity demand forecasting by using LEAP. The base year is 2010 and end year projection is 2025. The scenarios of simulated model consist of two scenarios. They are Business as Usual (BAU) and Government policy scenario. Results of both scenarios indicate that end year electricity demand forecasting in Indonesia increased more than two fold compared to base year.

State prediction for the actuators of civil aircraft based on a fusion framework of relevance vector machine and autoregressive integrated moving average

2018 ◽  
Vol 232 (5) ◽  
pp. 622-634 ◽  
Author(s):  
Runxia Guo ◽  
Jiaqi Wang ◽  
Na Zhang ◽  
Jiankang Dong
Keyword(s):  
Moving Average ◽  
Relevance Vector Machine ◽  
Average Model ◽  
Autoregressive Integrated Moving Average ◽  
State Prediction ◽  
Civil Aircraft ◽  
Moving Average Model ◽  
Long Term Prediction ◽  
Fusion Framework

Relevance vector machine is a newly proposed and effective state prediction algorithm proved by practical applications; however, the accuracy of the single relevance vector machine model for the long-term prediction is unable to achieve satisfactory results with time goes by. Then, an autoregressive integrated moving average model is introduced to correct the prediction error caused by the single relevance vector machine, and a fusion framework based on the combination of relevance vector machine and autoregressive integrated moving average model is adopted to improve the accuracy of long-term prediction. In addition, a targeted approach for retraining the old model is put forward so that the state prediction model can be updated in time and suits the actual situation better. The effectiveness of the proposed fusion framework is illustrated via an aircraft actuator, and the experiments based on a model of civil aircraft actuator data set show that the proposed method yields a satisfied performance in state prediction of aircraft actuators.

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