SEA: A Combined Model for Heat Demand Prediction

TL;DR

This paper introduces SEA, a combined model using STL decomposition with neural networks and ARIMA to improve heat demand prediction accuracy in energy networks.

Contribution

The paper proposes a novel combined model, STL-ENN-ARIMA (SEA), integrating neural networks and ARIMA for more accurate heat demand forecasting.

Findings

SEA outperforms individual models in prediction accuracy

The combined approach effectively captures seasonal and trend components

Experimental results show promising performance of SEA

Abstract

Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decompose the series into seasonal and trend components. Then, predicting the seasonal and trend components respectively, and combining their predictions together as the heat demand prediction is a possible way to predict heat demand. In this paper, STL-ENN-ARIMA (SEA), a combined model, was proposed based on the combination of the Elman neural network (ENN) and the autoregressive integrated moving average (ARIMA) model, which are commonly applied to heat demand prediction. ENN and ARIMA are used to predict seasonal and trend components, respectively. Experimental results demonstrate that the proposed SEA model has a promising performance.