Synthetic time series generation is an emerging field of study in the broad spectrum of data science, addressing critical needs in diverse fields such as finance, meteorology, and healthcare. In recent years, diffusion methods have shown impressive results for image synthesis thanks to models such as Stable Diffusion and DALL·E, defining the new state-of-the-art methods. In time series generation, their potential exists but remains largely unexplored. In this work, we demonstrate the applicability and suitability of diffusion methods for time series generation on several datasets with a rigorous evaluation procedure. Our proposal, inspired from an existing diffusion model, obtained a better performance than a reference model based on generative adversarial networks (GANs). We also propose a modification of the model to allow for guiding the generation with respect to conditioning variables. This conditioned generation is successfully demonstrated on meteorological data.

Building operation is responsible for 28% of the world’s carbon emissions. In this context, establishing priorities in refurbishment strategies at the scale of a city or a group of buildings is important. Such procedures are usually led by experts in energy performance and, therefore, they are rarely carried out due to their long and costly nature. This research aims at the estimation of building energy performance to pave the way towards finding near-optimal refurbishment strategies. Thanks to the identification of easily-accessible building characteristics, the method applies machine learning models to scan a building portfolio based on a low level of details. The results show good potential to identify low-performer buildings with simple machine learning methods. It also opens the door for further improvements through the inclusion of supplementary building features at the input of the predictive system. This work includes (a) the integration of a knowledge database thanks to the Swiss CECB energy performance certificates, referencing more than 70 000 buildings, (b) the preparation of a training data set through the selection of relevant physical characteristics of buildings (input) and the corresponding energy consumption labels (output), (c) the development of predictive models used in a supervised way, (d) their evaluation on an independent test set.

Recently, time-series forecasting foundation models trained on large, diverse datasets have demonstrated robust zero-shot and few-shot capabilities. Given the ubiquity of time-series data in IoT, finance, and industrial applications, rigorous benchmarking is essential to assess their forecasting performance and overall value. In this study, our objective is to benchmark foundational models from Amazon, Salesforce, and Google against traditional statistical and deep learning baselines on both public and proprietary industrial datasets. We evaluate zero-shot, few-shot, and full-shot scenarios using metrics such as sMAPE and NMAE on fine-tuned models, ensuring reliable comparisons. All experiments are conducted with onTime, our dedicated open-source library that guarantees reproducibility, data privacy, and flexible configuration. Our results show that foundation models often outperform traditional methods with minimal dataset-specific tuning, underscoring their potential to simplify forecasting tasks and bridge performance gaps in data-scarce settings. Additionally, we address non-performance criteria—such as integration ease, model size, and inference/training time, which are critical for real-world deployment.

Modeling building behavior is important to enhance energy management and occupant comfort. Traditional simulation methods for district heating networks (DHNs) often struggle with scalability and computational efficiency. This paper explores diffusion models, a state-of-the-art deep learning approach, to generate conditioned multivariate time series data for DHNs, utilizing public building data and telemetry. Preliminary results suggest that diffusion models have the potential to capture statistical properties of time series, especially for durations shorter than 24 hours. While conditional generation did follow the real data in terms of mean trend and variance, challenges remain in accurately reproducing peaks and extreme values. We discuss potential improvements in embedding methods, evaluation, and model architectures to enhance robustness. Our findings highlight the promising use of diffusion models for DHN simulations.

This paper presents an innovative methodology for enhancing energy efficiency assessment procedures in the built environment, with a focus on the Switzerland’s Energy Strategy 2050. The current methodology necessitates intensive expert surveys, leading to substantial time and cost implications. Also, such a process can’t be scaled to a large number of buildings. Using machine learning techniques, the estimation process is augmented and exploit open data resources. Utilizing a robust dataset exceeding 70’000 energy performance certificates (CECB), the method devises a two-stage ML approach to forecast energy performance. The first phase involves data reconstruction from online repositories, while the second employs a regression algorithm to estimate the energy efficiency. The proposed approach addresses the limitations of existing machine learning methods by offering finer prediction granularity and incorporating readily available data. The results show a commendable degree of prediction accuracy, particularly for single-family residences. Despite this, the study reveals a demand for further granular data, and underlines privacy concerns associated with such data collection. In summary, this investigation provides a significant contribution to the enhancement of energy efficiency assessment methodologies and policy-making.