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

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. [ABSTRACT FROM AUTHOR]