Apply Bayesian Inference with Normal–Normal Conjugate to Forecast Renewable Energy Generation: A Case Study of Waste-to-Energy in Taiwan

Year 2024, Volume: 4 Issue: 2, 50 - 56, 24.06.2024

Yu-Jen Lin

https://doi.org/10.5152/tepes.2024.24004

https://izlik.org/JA95KD83BB

Abstract

This paper applies Bayesian inference with normal–normal conjugate to forecast renewable energy generation. The generation forecasts a probability distribution rather than a quantitative value. An assumed normal distribution is initialized for renewable energy generation. This assumed normal distribution’s parameters, the mean μ, and the standard deviation σ, are inferred by Bayesian inference afterward. However, applying Bayesian inference barely shall encounter an intractable integral. To circumvent the intractable integral, this paper considers the normal-normal conjugate method. This method fixes the assumed normal distribution’s σ and characterizes μ as another normal distribution and then infers the latter normal distribution parameters. A case study of waste-to-energy generation forecast in Taiwan is investigated in this paper. It has been found from the investigation that the Bayesian inferred probability distribution outperforms the assumed one.

Keywords

Bayesian inference , forecast , normal–normal conjugate , renewable energy generation , waste-to-energy

Supporting Institution

This study funded by the I-Shou University Taiwan R.O.C. under grand ISU-112-02-01A.

Project Number

ISU-112-02-01A.

Thanks

The author would like to express his gratitide to I-Shou University, Taiwan, R.O.C. under Grand ISU-112-02-01A.

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