Advancing Heat Demand Forecasting with Attention Mechanisms: Opportunities and Challenges - Ramachandran, Adithya; Neergaard, Thorkil F.B.; Maier, Andreas; Bayer, Siming (Advancing Heat Demand Forecasting with Attention Mechanisms: Opportunities and Challenges, 2024)
Global leaders and policymakers are unified in their unequivocal commitment to decarbonization efforts in support of Net-Zero agreements. District Heating Systems (DHS), while contributing to carbon emissions due to the continued reliance on fossil fuels for heat production, are embracing more sustainable practices albeit with some sense of vulnerability as it could constrain their ability to adapt to dynamic demand and production scenarios. As demographic demands grow and renewables become the central strategy in decarbonizing the heating sector, the need for accurate demand forecasting has intensified. Advances in digitization have paved the way for Machine Learning (ML) based solutions to become the industry standard for modeling complex time series patterns. In this paper, we focus on building a Deep Learning (DL) model that uses deconstructed components of independent and dependent variables that affect heat demand as features to perform multi-step ahead forecasting of head demand. The model represents the input features in a time-frequency space and uses an attention mechanism to generate accurate forecasts. The proposed method is evaluated on a real-world dataset and the forecasting performance is assessed against LSTM and CNN-based forecasting models. Across different supply zones, the attention-based models outperforms the baselines quantitatively and qualitatively, with an Mean Absolute Error (MAE) of 0.105 ± 0.06kWh and a Mean Absolute Percentage Error (MAPE) of 5.4% ± 2.8%, in comparison the second best model with a MAE of 0.10 ± 0.06kWh and a MAPE of 5.6% ± 3

A Week Ahead Water Demand Forecasting using Convolutional Neural Network on Multi-Channel Wavelet Scalogram - Ramachandran, Adithya; Mousa, Hatem; Maier, Andreas; Bayer, Siming (WDSA CCWI 2024 - 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry, 2024)

Creating a labeled district heating data set: From anomaly detection towards fault detection - Stecher, Dominik; Neumayer, Martin; Ramachandran, Adithya; Hort, Anastasia; Maier, Andreas; Bücker, Dominikus; Schmidt, Jochen (Energy, 2024)
For an efficient operation of district heating systems, being able to detect anomalies and faults at an early stage is highly desirable. Here, data-driven machine learning methods can be a cornerstone, particularly for fault detection in district heating substations, where the availability of heat meter data keeps increasing. However, the creation of data sets suitable for training such machine learning models poses challenges to researchers and practitioners alike. To address this problem, we propose a systematic and domain-specific process for data set creation for fault detection in the form of practical guidelines. This process concretizes the data science and data mining cross-industry standard CRISP-DM for the district heating domain and focuses on the process steps of goal definition, data acquisition and understanding, and data curation. We aim to enable researchers and practitioners to create data sets for fault detection in the district heating domain and therefore also enable the creation or improvement of machine learning models in this domain. In addition, we propose a minimum viable feature set for fault detection in district heating networks with the goal of enabling better cooperation between researchers and easier transfer of the resulting machine learning models, to better proliferate new progress in the field.
CRIS-Team Scopus Importer:2025-02-04
Keywords: Data augmentation; Data set creation; District heating; Fault detection; Machine learning

Digitization Framework of Water Utility Documents for Digital Twins - Ramachandran, Adithya; Maier, Andreas; Bayer, Siming (Digitization Framework of Water Utility Documents for Digital Twins, 2025)

Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble - Chatterjee, Satyaki; Ramachandran, Adithya; Neergaard, Thorkil F.B.; Maier, Andreas; Bayer, Siming (NeurIPS 2022 Workshop: Tackling Climate Change with Machine Learning, 2022)
One of the primal challenges faced by utility companies is ensuring efficient supply with minimal greenhouse gas emissions. The advent of smart meters and smart grids provide an unprecedented advantage in realizing an optimised supply of thermal energies through proactive techniques such as load forecasting. In this paper, we propose a forecasting framework for heat demand based on neural networks where the time series are encoded as scalograms equipped with the capacity of embedding exogenous variables. Subsequently, CNNs are utilized to predict the heat load multi-step ahead. Finally, the proposed framework is compared with other state-of-the-art methods, such as SARIMAX and LSTM. The quantitative results from retrospective experiments show that the proposed framework consistently outperforms the state-of-the-art baseline method with real-world data acquired from Denmark. A minimal mean error of 7.54% for MAPE and 417 kW for RMSE is achieved with the proposed framework in comparison to all other methods.<br
Keywords: CNN; District Heating; Time Serie Forecasting

Link Prediction on Water Distribution Networks using Graph Neural Networks - Pahlajani, Karan Mahes; Ramachandran, Adithya; Maier, Andreas; Bayer, Siming (Link Prediction on Water Distribution Networks using Graph Neural Networks, 2025)

Unveiling Consumer Behavior in District Heating Network: A Contrastive Learning Approach to Clustering - Basak, Pooja; Ramachandran, Adithya; Maier, Andreas; Bayer, Siming (SESAAU2024 – Smart Energy Systems Conference, 2024)

Water Demand Forecasting of District Metered Areas through Learned Consumer Representations - Ramachandran, Adithya; Arias Vergara, Tomás; Maier, Andreas; Bayer, Siming (Water Demand Forecasting of District Metered Areas through Learned Consumer Representations, 2025)