Publication detail

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

TENG, S. MÁŠA, V. TOUŠ, M. VONDRA, M. LAM, H.L., STEHLÍK, P.

Original Title

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

Type

journal article in Web of Science

Language

English

Original Abstract

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 fore-casting 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. (c) 2021 Elsevier Ltd. All rights reserved.

Keywords

Waste-to-energy, Energy forecasting, Energy optimization, Hierarchical temporal memory (HTM), Machine learning, Neural networks

Authors

TENG, S.; MÁŠA, V.; TOUŠ, M.; VONDRA, M.; LAM, H.L., STEHLÍK, P.

Released

3. 1. 2022

Publisher

Elsevier

Location

Oxford, England

ISBN

0960-1481

Periodical

RENEWABLE ENERGY

Year of study

181

Number

1

State

United Kingdom of Great Britain and Northern Ireland

Pages from

142

Pages to

155

Pages count

14

URL

https://www.sciencedirect.com/science/article/pii/S0960148121013252

BibTex

@article{BUT175245,
  author="Sin Yong {Teng} and Vítězslav {Máša} and Michal {Touš} and Marek {Vondra} and Petr {Stehlík}",
  title="Waste-to-energy forecasting and real-time optimization: An anomaly-aware approach",
  journal="RENEWABLE ENERGY",
  year="2022",
  volume="181",
  number="1",
  pages="142--155",
  doi="10.1016/j.renene.2021.09.026",
  issn="0960-1481",
  url="https://www.sciencedirect.com/science/article/pii/S0960148121013252"
}