Multivariate Ensemble Forecast Framework for Demand Prediction of Anomalous Days

• Published in: IEEE transactions on sustainable energy 2020-01, Vol.11 (1), p.27-36
• Main Authors: Raza, Muhammad Qamar, Mithulananthan, Nadarajah, Li, Jiaming, Lee, Kwang Y.
• Format: Article
• Language: English
• Subjects: Anomalies; Artificial neural networks; Back propagation networks; Demand side management; Economic forecasting; Electric industries; Electricity consumption; Elman neural network (ELM); Energy management; Energy resources; Energy sources; ensemble forecast framework (ENFF); feedforward neural network (FNN); Forecasting; global best particle swarm optimization (GPSO); Load forecasting; Load modeling; Neural networks; Particle swarm optimization; Penetration; Predictive models; Radial basis function; radial basis function (RBF); Renewable energy; Renewable resources; Short term load forecasting (STLF); Supply-demand forecasting; Training; trim aggregation; Weather forecasting
• ISSN: 1949-3029; 1949-3037
• DOI: 10.1109/TSTE.2018.2883393

An accurate load forecast is always important for the power industry and energy players as it enables stakeholders to make critical decisions. In addition, its importance is further increased with growing uncertainties in the generation sector due to the high penetration of renewable energy and the introduction of demand side management strategies. An incremental improvement in grid-level demand forecast of anomalous days can potentially save millions of dollars. However, due to an increasing penetration of renewable energy resources and their dependency on several meteorological and exogenous variables, accurate load forecasting of anomalous days has now become very challenging. To improve the prediction accuracy of the load forecasting, an ensemble forecast framework (ENFF) is proposed with a systematic combination of three multiple predictors, namely Elman neural network, feedforward neural network, and radial basis function neural network. These predictors are trained using global particle swarm optimization to improve their prediction capability in the ENFF. The outputs of individual predictors are combined using a trim aggregation technique by removing forecasting anomalies. Real recorded data of New England ISO grid is used for training and testing of the ENFF for anomalous days. The forecast results of the proposed ENFF indicate a significant improvement in prediction accuracy in comparison to autoregressive integrated moving average and back-propagation neural networks based benchmark models.