# Prediction of Household-level Heat-Consumption using PSO enhanced SVR Model

arXiv: https://arxiv.org/abs/2112.01908 · HTML: https://mlsanity.com/paper/2112.01908/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 17 references that are in the map.
- First posted on arXiv 3 Dec 2021.

## Built on by (most cited first, 3 of 3)

- [Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble](https://mlsanity.com/paper/2210.13108/) (2022)
- [A Deep Learning Framework for Heat Demand Forecasting using Time-Frequency Representations of Decomposed Features](https://mlsanity.com/paper/2603.01137/) (2026)
- [Water Demand Forecasting of District Metered Areas through Learned Consumer Representations](https://mlsanity.com/paper/2509.07515/) (2025)

## Abstract (arXiv)

In combating climate change, an effective demand-based energy supply operation of the district energy system (DES) for heating or cooling is indispensable. As a consequence, an accurate forecast of heat consumption on the consumer side poses an important first step towards an optimal energy supply. However, due to the non-linearity and non-stationarity of heat consumption data, the prediction of the thermal energy demand of DES remains challenging. In this work, we propose a forecasting framework for thermal energy consumption within a district heating system (DHS) based on kernel Support Vector Regression (kSVR) using real-world smart meter data. Particle Swarm Optimization (PSO) is employed to find the optimal hyper-parameter for the kSVR model which leads to the superiority of the proposed methods when compared to a state-of-the-art ARIMA model. The average MAPE is reduced to 2.07% and 2.64% for the individual meter-specific forecasting and for forecasting of societal consumption, respectively.