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A Deep Learning Framework for Heat Demand Forecasting using Time-Frequency Representations of Decomposed Features

Adithya Ramachandran, Satyaki Chatterjee, Thorkil Flensmark B. Neergaard, Maximilian Oberndoerfer, Andreas Maier, Siming Bayer

TL;DR

This work proposes a novel deep learning framework for day-ahead heat demand prediction that leverages time-frequency representations of historical data and enables Convolutional Neural Networks to learn hierarchical temporal features that are often inaccessible to standard time domain models.

Abstract

District Heating Systems are essential infrastructure for delivering heat to consumers across a geographic region sustainably, yet efficient management relies on optimizing diverse energy sources, such as wood, gas, electricity, and solar, in response to fluctuating demand. Aligning supply with demand is critical not only for ensuring reliable heat distribution but also for minimizing carbon emissions and extending infrastructure lifespan through lower operating temperatures. However, accurate multi-step forecasting to support these goals remains challenging due to complex, non-linear usage patterns and external dependencies. In this work, we propose a novel deep learning framework for day-ahead heat demand prediction that leverages time-frequency representations of historical data. By applying Continuous Wavelet Transform to decomposed demand and external meteorological factors, our approach enables Convolutional Neural Networks to learn hierarchical temporal features that are often inaccessible to standard time domain models. We systematically evaluate this method against statistical baselines, state-of-the-art Transformers, and emerging foundation models using multi-year data from three distinct Danish districts, a Danish city, and a German city. The results show a significant advancement, reducing the Mean Absolute Error by 36% to 43% compared to the strongest baselines, achieving forecasting accuracy of up to 95% across annual test datasets. Qualitative and statistical analyses further confirm the accuracy and robustness by reliably tracking volatile demand peaks where others fail. This work contributes both a high-performance forecasting architecture and critical insights into optimal feature composition, offering a validated solution for modern energy applications.

A Deep Learning Framework for Heat Demand Forecasting using Time-Frequency Representations of Decomposed Features

TL;DR

This work proposes a novel deep learning framework for day-ahead heat demand prediction that leverages time-frequency representations of historical data and enables Convolutional Neural Networks to learn hierarchical temporal features that are often inaccessible to standard time domain models.

Abstract

District Heating Systems are essential infrastructure for delivering heat to consumers across a geographic region sustainably, yet efficient management relies on optimizing diverse energy sources, such as wood, gas, electricity, and solar, in response to fluctuating demand. Aligning supply with demand is critical not only for ensuring reliable heat distribution but also for minimizing carbon emissions and extending infrastructure lifespan through lower operating temperatures. However, accurate multi-step forecasting to support these goals remains challenging due to complex, non-linear usage patterns and external dependencies. In this work, we propose a novel deep learning framework for day-ahead heat demand prediction that leverages time-frequency representations of historical data. By applying Continuous Wavelet Transform to decomposed demand and external meteorological factors, our approach enables Convolutional Neural Networks to learn hierarchical temporal features that are often inaccessible to standard time domain models. We systematically evaluate this method against statistical baselines, state-of-the-art Transformers, and emerging foundation models using multi-year data from three distinct Danish districts, a Danish city, and a German city. The results show a significant advancement, reducing the Mean Absolute Error by 36% to 43% compared to the strongest baselines, achieving forecasting accuracy of up to 95% across annual test datasets. Qualitative and statistical analyses further confirm the accuracy and robustness by reliably tracking volatile demand peaks where others fail. This work contributes both a high-performance forecasting architecture and critical insights into optimal feature composition, offering a validated solution for modern energy applications.
Paper Structure (37 sections, 5 equations, 20 figures, 17 tables)

This paper contains 37 sections, 5 equations, 20 figures, 17 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Statistical and Machine Learning
  4. Deep Learning Architectures
  5. Contributions
  6. Methodology
  7. Data Overview
  8. Data Preprocessing
  9. Data Analysis
  10. Feature Representation
  11. Forecasting Model
  12. Experimental Setup
  13. Dataset
  14. Architecture
  15. Evaluation Strategy
  16. ...and 22 more sections

Figures (20)

  • Figure 1: Hourly heat consumption data over the period from January 2016 to the end of December 2019 for DMA A. The outliers observed in the data identified through the Savitzky-Golay smoothing are marked using orange circles.
  • Figure 2: Preprocessed hourly heat consumption data for DMA A. (a) Data from January 2016 to December 2019 after outlier removal. (b) A detailed view of the recorded heat consumption for two weeks in January 2017.
  • Figure 3: The distribution of Spearman correlation coefficients $\rho$ between successive days of the week across different dma.
  • Figure 4: Short-term and long-term correlational analysis of heat demand data. (a) Spearman correlation $\rho$ between heat demand data and the lagged version of the heat demand data. Hourly lags equivalent to $1, 2,...,28$ days are considered to evaluate short-term relationships. (b) Spearman correlation $\rho$ of weekly demand across 204 weeks comprised in the four-year heat demand dataset across different dmas.
  • Figure 5: Spearman correlation coefficients between heat demand and key weather features across different dmas. The heatmap(s) are structured in four rows, displaying correlations between: (1) actual heat demand and actual weather data; (2) their respective trend components; (3) their respective seasonal components; and (4) their respective residual components.
  • ...and 15 more figures