Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization

Sen, P.K.; Roy, Mousumi; Pal, Parimal

doi:10.1016/j.energy.2016.10.068

Cited by 196 publications

(99 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Several methods have studied the problem of load and energy forecasting by using regression analysis and neural network. [9][10][11] A model to predict heat demand based on temperature and historical data of natural gas consumption is developed in ref. 12 This model predicts the expected heat demand 1 day in advance.…”

Section: Introductionmentioning

confidence: 99%

Structure dependent weather normalization

Beheshti

Sahebalam

Nidoy

2019

Energy Science & Engineering

View full text Add to dashboard Cite

In this paper, we introduce a new analytical method to normalize and forecast the energy usage/loss of residential and commercial buildings. Weather conditions have large effects on energy and economic activity. Weather Normalization is an important step in building energy rating and retrofit measurements. It has also become increasingly important because of changes in the worlds weather patterns due to global warming. Accounting for the impacts of weather on energy use in buildings is an extremely exhaustive challenge because of the complexity and diversity in the operation of the mechanical and electrical systems. In traditional weather normalization methods some building parameters, such as building size, window size, construction joints, and the effect of flues, are missing. We present a Structure Dependent Energy Usage/Loss (SDE U/L) linear and nonlinear models by using Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) to capture and forecast the behavior of energy consumption/loss. This model considers different building and weather parameters. Using the (SDE U/L) model, we present an innovative approach for linear and nonlinear weather normalization. Our simulation results demonstrate the flexibility and advantages of our structure dependent weather normalization method. Unlike most existing methods, the (SDE U/L) method does not impose any constraints on a property on its property type, use details, and energy data to be able to perform weather normalization for any building over time.

show abstract

Section: Introductionmentioning

confidence: 99%

Structure dependent weather normalization

Beheshti

Sahebalam

Nidoy

2019

Energy Science & Engineering

View full text Add to dashboard Cite

show abstract

“…India is a major energy consumer. In current research, many reviews of energy consumption in different Indian sectors have been made by researchers [10][11][12][13][14][15][16]. Besides, there are many valuable works on energy forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Sen et al used ARIMA to forecast the energy consumption of an Indian pig iron manufacturing organization, the results of which appeared smoother than the seasonal random trend model [10]. The ARIMA model has been improved greatly.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model

Jiang

Yang

2018

Sustainability

View full text Add to dashboard Cite

Better prediction of energy demand is of vital importance for developing countries to develop effective energy strategies to improve energy security, partly because those countries' energy demands are increasing rapidly. In this work, metabolic grey model (MGM), autoregressive integrated moving average (ARIMA), MGM-ARIMA, and back propagation neural network (BP) are adopted to forecast energy demand in India, the third largest energy consumer in the world after China and the USA. The average relative errors between the actual and simulated value are 1.31% (MGM), 1.07%, 0.92% (MGM-ARIMA), and 0.39% (BP). The high prediction accuracy indicates that the prediction result is effective. The result shows that India's energy consumption will increase by 4.75% a year in the next 14 years. Compared with the 5.1% per year on average in 1995-2016, India's energy consumption will still continue its steady growth at about 5% growth from 2017 to 2030.

show abstract

“…On the other hand, if |θ| ≤ 1, then the series Y t is considered stationary. The stationarity series is checked at first stage because correlation might exist in non-stationary time series and even in very large sample which results into the spurious (or nonsense) regression [7,72]. From Equation (1) it is inferred that wherever the issue of unit root arises in the time series, the differencing is done and is therefore indicated as the order of integration for each series.…”

Section: Autoregressive Integrated Moving Average (Arima) and Holt-wimentioning

confidence: 99%

An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

et al. 2017

View full text Add to dashboard Cite

Abstract:Energy planning and policy development require an in-depth assessment of energy resources and long-term demand forecast estimates. Pakistan, unfortunately, lacks reliable data on its energy resources as well do not have dependable long-term energy demand forecasts. As a result, the policy makers could not come up with an effective energy policy in the history of the country. Energy demand forecast has attained greatest ever attention in the perspective of growing population and diminishing fossil fuel resources. In this study, Pakistan's energy demand forecast for electricity, natural gas, oil, coal and LPG across all the sectors of the economy have been undertaken. Three different energy demand forecasting methodologies, i.e., Autoregressive Integrated Moving Average (ARIMA), Holt-Winter and Long-range Energy Alternate Planning (LEAP) model were used. The demand forecast estimates of each of these methods were compared using annual energy demand data. The results of this study suggest that ARIMA is more appropriate for energy demand forecasting for Pakistan compared to Holt-Winter model and LEAP model. It is estimated that industrial sector's demand shall be highest in the year 2035 followed by transport and domestic sectors. The results further suggest that energy fuel mix will change considerably, such that oil will be the most highly consumed energy form (38.16%) followed by natural gas (36.57%), electricity (16.22%), coal (7.52%) and LPG (1.52%) in 2035. In view of higher demand forecast of fossil fuels consumption, this study recommends that government should take the initiative for harnessing renewable energy resources for meeting future energy demand to not only avert huge import bill but also achieving energy security and sustainability in the long run.

show abstract

Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization

Cited by 196 publications

References 24 publications

Structure dependent weather normalization

Structure dependent weather normalization

Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model

An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

Contact Info

Product

Resources

About