Article: An intelligent timestamp data manipulation methodology for customer level resource efficient short-term electric load forecasting Journal: International Journal of Data Analysis Techniques and Strategies (IJDATS) 2023 Vol.15 No.3 pp.198 - 216 Abstract: Electricity supply companies need to know the expected load consumption to perform better scheduling and planning. Prediction of electric-load usage thus becomes a time-series forecasting problem. Traditionally used statistical, knowledge-based and hybrid techniques for forecasting do not ensure a high level of accuracy, while more accurate techniques like deep learning are computationally expensive and require additional non-temporal data. We propose a significantly accurate but computationally efficient methodology using 'stack ensembling' with two different data manipulations and compare their results with selected baseline predictors as well as existing literature. We only use timestamp information for feature extraction to keep this study independent of non-time features. We achieve a maximum improvement of 34.98% in terms of MAPE over chosen base predictor ARIMA. Limitations of our work include a low degree of accuracy for outliers' estimation in the electric-load consumption on which we plan to improve upon in future work. Inderscience Publishers - linking academia, business and industry through research

Title: An intelligent timestamp data manipulation methodology for customer level resource efficient short-term electric load forecasting

Authors: Ali Waqas; Muhammad Saleem; Abdul Khaliq; Amanullah Yasin

Addresses: Sir Syed CASE Institute of Technology, Street 33, Block A, Multi Gardens Sector B-17, Islamabad, Pakistan ' Sir Syed CASE Institute of Technology, Street 33, Block A, Multi Gardens Sector B-17, Islamabad, Pakistan ' Sir Syed CASE Institute of Technology, Street 33, Block A, Multi Gardens Sector B-17, Islamabad, Pakistan ' Air University, Service Road E-9/E-8, Islamabad, Pakistan

Abstract: Electricity supply companies need to know the expected load consumption to perform better scheduling and planning. Prediction of electric-load usage thus becomes a time-series forecasting problem. Traditionally used statistical, knowledge-based and hybrid techniques for forecasting do not ensure a high level of accuracy, while more accurate techniques like deep learning are computationally expensive and require additional non-temporal data. We propose a significantly accurate but computationally efficient methodology using 'stack ensembling' with two different data manipulations and compare their results with selected baseline predictors as well as existing literature. We only use timestamp information for feature extraction to keep this study independent of non-time features. We achieve a maximum improvement of 34.98% in terms of MAPE over chosen base predictor ARIMA. Limitations of our work include a low degree of accuracy for outliers' estimation in the electric-load consumption on which we plan to improve upon in future work.

Keywords: ARIMA; calendar features; electricity; ensembling; forecasting; intelligent data manipulation; load prediction; machine learning; planning; persistence; scheduling; stacking.

DOI: 10.1504/IJDATS.2023.133011

International Journal of Data Analysis Techniques and Strategies, 2023 Vol.15 No.3, pp.198 - 216

Received: 23 Mar 2022
Received in revised form: 15 Sep 2022
Accepted: 24 Dec 2022
Published online: 24 Aug 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: An intelligent timestamp data manipulation methodology for customer level resource efficient short-term electric load forecasting

Keep up-to-date