Designing a Robust and Accurate Model for Consumer Centric Short Term Load Forecasting in Microgrid Environment

• Published in: IEEE systems journal Vol. 16; no. 2; pp. 1 - 12
• Main Authors: A. Muzumdar, Ajit, N. Modi, Chirag, G. M, Madhu, Vyjayanthi, Chintamani
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-06-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Autoregressive processes; Decision making; Decision trees; Distributed generation; Electrical loads; Energy consumption; Energy distribution; Energy management; Error reduction; Forecasting; Hidden Markov models; Load forecasting; Load modeling; long short-term memory (LSTM); Machine learning; Mathematical models; microgrid; Predictive models; random forest; residential load forecasting; Robustness; short term load forecasting (STLF); Substations; support vector regressor (SVR); weighted average
• ISSN: 1932-8184; 1937-9234
• DOI: 10.1109/JSYST.2021.3073493

The consumer's short term load forecasting plays an essential role in microgrid energy distribution. However, the load forecasting at consumer level is more challenging than at substation level due to high volatility and uncertainty in energy consumption. In literature, many machine learning-based forecasting models have been explored. However, there is a need of developing a robust and accurate model to handle highly inconsistent energy consumption. In this article, we propose a robust and accurate model for consumer's short term load forecasting, which uses feasible techniques such as random forest, support vector regressor, and long short term memory as base predictors to handle varying traits of energy consumption. For the final decision-making on forecasting result of these predictors, it assigns weights to each predictor dynamically as per the forecasting efficacy. The proposed model is tested on different consumer's varying traits of energy consumption. The experimental results show that the proposed model achieves forecasting error reduction by 3.46 and 2.53 in terms of average RMSE and MAE, respectively, in comparison with the existing models. It is robust and accurate even in presence of highly volatile and uncertain load patterns, and thus, it can better fit for microgrid energy management.