Please use this identifier to cite or link to this item:
Title: Forecasting of energy consumption in an industrial firm using statistical and machine learning models
Authors: Suranga, H. W. A.
Karunathunga, N.
Perera, S. S. N.
De Silva, S. A. K. P.
Keywords: Energy forecasting
Machine learning
Deep learning
Vector auto regression
Issue Date: 3-May-2023
Publisher: South Eastern University of Sri Lanka Oluvil, Sri Lanka
Citation: 11th International Symposium (IntSym 2023) Managing Contemporary Issues for Sustainable Future through Multidisciplinary Research Proceedings 03rd May 2023 South Eastern University of Sri Lanka p. 111-118..
Abstract: Manufacturing industry is a key contributor to the economy. These manufacturing firms are high energy consumers and currently facing challenges in their energy demand due to scarcity, cost, subsequent environmental impact on heavy consumption, social and consumer pressure for carbon footprint of consumer goods and industry specific global compliances and regulations. Then an accurate short-term forecast of energy consumption is a must to maintain optimal supply and usage while minimizing negative concerns against higher consumption. This will enable a smooth operation with a minimum risk on energy related inventories. Electricity, biomass, furnace oil and diesel are the main energy sources in industry. In current context, studies are conducting on electricity consumption in industries, buildings, and residential boundaries. However, predictions on total energy consumption on manufacturing firms are not frequently studied. Both conventional statistical models and deep learning models are widely used for this task. In this exercise, a manufacturing entity has been selected to predict its forthcoming month’s energy consumption using historical energy consumption and manufacturing figures. Existing literature related to energy consumption prediction suggested, four models to predict energy consumptions. Fb prophet, vector auto regression, Long Sort Term Memory (LSTM) and Auto Regressive Integrated Moving Average (ARIMA) models were taken to make predictions for energy consumption in terms of electricity and biomass. Performance of each models were compared using root mean squared error, mean absolute error and mean percentage absolute error. LSTM outperformed over all other models for both types of energies and the same model was selected to predict monthly energy consumption of selected entity.
ISBN: 978-955-627-013-6
Appears in Collections:11th International Symposium - 2023

Files in This Item:
File Description SizeFormat 
IntSym 2023 Proceedings-111-118.pdf673.94 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.