Multivariate Short Term Load Forecasting Strategy: Application to Anomalous Days of ISO New England Data

Innocent Duma, Bhekisipho Twala

2021

Abstract

In this paper, we consider short-term electricity load forecasting which is for making forecasting within 1 hour to 7 days or a month ahead usually used for the day-to-day operations of the utility industry, such as scheduling the generation and transmission of electric energy. This is a three step process: (1) Data preprocessing which include feature extraction, (2) Modeling and (3) Model Evaluation. Electrical load time series are non stationary and notoriously very noisy because of variety of factors that affect the electrical markets. As a data preprocessing step to remove the white noise on the multivariate predictor variables (which include historical load, weather, and holidays) we perform a multivariate denoising using wavelets and principal component analysis (MWPCA). In the modeling step we propose three multivariate Bayesian Optimization (BO) based Random Forest (RF), Feedforward Neural Networks (FFNN) and Long Short-term Memory (LSTM) neural network for day ahead hourly load forecast of the anomalous days system load of the ISO New England grid. For model evaluation we used three evaluation metrics, the Mean Absolute Percent Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). All the trained models achieved a superior results on the chosen model evaluation metrics most notably achieving a MAPE of less than 1% on the data under study. And the FFNN model outperformed both the RF and LSTM models.

Paper Citation

in Harvard Style

Duma I. and Twala B. (2021). Multivariate Short Term Load Forecasting Strategy: Application to Anomalous Days of ISO New England Data. In Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - Volume 1: NCTA; ISBN 978-989-758-534-0, SciTePress, pages 293-301. DOI: 10.5220/0010652600003063

in Bibtex Style

@conference{ncta21,
author={Innocent Duma and Bhekisipho Twala},
title={Multivariate Short Term Load Forecasting Strategy: Application to Anomalous Days of ISO New England Data},
booktitle={Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - Volume 1: NCTA},
year={2021},
pages={293-301},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010652600003063},
isbn={978-989-758-534-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - Volume 1: NCTA
TI - Multivariate Short Term Load Forecasting Strategy: Application to Anomalous Days of ISO New England Data
SN - 978-989-758-534-0
AU - Duma I.
AU - Twala B.
PY - 2021
SP - 293
EP - 301
DO - 10.5220/0010652600003063
PB - SciTePress