Authors:
Innocent Duma
1
and
Bhekisipho Twala
2
Affiliations:
1
Department of Electrical Power Engineering, Faculty of Engineering and the Built Environment, Durban University of Technology, P.O. Box 1334, Durban, 4000, South Africa
;
2
Faculty of Engineering and the Built Environment, Durban University of Technology, P.O. Box 1334, Durban, 4000, South Africa
Keyword(s):
Short Term Load Forecasting, Multivariate Denoising using Wavelet and Principal Component Analysis, Bayesian Optimization Algorithm, Long Short-Term Memory Neural Networks, Feedforward Neural Networks, Random Forest.
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 l
oad 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.
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