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\title{CNN-LSTM Time Series Forecasting of Electricity Power Generation
Considering Biomass Thermal Systems}



\author[1]{William Buratto}%
\author[2]{Rafael  Muniz}%
\author[3]{Ademir Nied}%
\author[2]{Carlos  Barros}%
\author[2]{Rodolfo  Cardoso}%
\author[4]{Gabriel  Gonzalez}%
\affil[1]{UDESC}%
\affil[2]{Federal Fluminense University}%
\affil[3]{University of Santa Catarina State}%
\affil[4]{University of Salamanca}%


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  \date{April 25, 2026}


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\begin{abstract}
The use of biomass as a renewable energy source for electricity
generation has gained attention due to its sustainability and
environmental benefits. However, the intermittent electricity demand
poses challenges for optimizing electricity generation in thermal
systems. Time series forecasting techniques are crucial in addressing
these challenges by providing accurate predictions of biomass
availability and electricity generation. In this paper, convolutional
neural networks (CNN) are used to extract features of the time series,
and long short-term memory (LSTM) is applied to perform the predictions.
The result of the mean absolute percentage error equal to 0.02562 shows
that the CNN-LSTM is a promising machine learning methodology for
electricity generation forecasting. Additionally, this paper discusses
the importance of model evaluation techniques and validation strategies
to assess the performance of forecasting models in real-world
applications. Finally, future research directions and potential
advancements in time series forecasting for biomass thermal systems are
outlined to foster continued innovation in sustainable energy
generation.%
\end{abstract}%



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