Waste-to-energy forecasting and real-time optimization: An anomaly-aware approach

• Published in: Renewable energy Vol. 181; pp. 142 - 155
• Main Authors: Teng, Sin Yong, Máša, Vítězslav, Touš, Michal, Vondra, Marek, Lam, Hon Loong, Stehlík, Petr
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2022
• Subjects: Energy forecasting; Energy optimization; Hierarchical temporal memory (HTM); Machine learning; Neural networks; Waste-to-energy; Waste-to-energy; Neural networks; Energy forecasting; Hierarchical temporal memory (HTM); Machine learning; Energy optimization
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2021.09.026

Waste-to-energy (WTE) technologies convert municipal solid, and biomass wastes into affordable renewable heat and power energy. However, there are large uncertainties associated with using waste feed as a renewable energy source. This paper proposes a WTE management tool that provides forecasting and real-time optimization of power generated with the consideration of anomaly. The WTE management framework was designed based on a biological neural network, the Hierarchical Temporal Memory (HTM) coupled with a dual-mode optimization procedure. The HTM model is inspired by the mechanism in the cerebral neocortex of the brain, providing anomaly identification and spatial-temporal prediction. In this work, the HTM-based smart energy framework is demonstrated in an industrial case study for the power generation of a waste-to-energy cogeneration system. HTM was compared with methods such as Long Short-Term Memory (LSTM) neural network, Autoregressive Integrated Moving Average (ARIMA), Fourier Transformation Extrapolation (FTE), persistence forecasting, and was able to achieve mean squared error (MSE) of 0.08466% while giving 35450 Euro profit in half a year. Coupled with a novel dual-mode optimization procedure, HTM demonstrated 11% improvement with respect to only predictive optimization (with HTM) in estimated gross profit. [Display omitted] •A practical, effective and robust smart energy management framework was presented.•A neocortex-inspired algorithm, Hierarchical Temporal Memory was used for prediction.•An industrial cogeneration plant in Czech Republic was used as the case study.•HTM predicted demands better than LSTM, ARIMA and Fourier analysis (MSE = 0.8466%).•Novel dual-mode optimization was used which achieved 27.3% improvement in gross profit.