Technology Best of What's New 5 home innovations that improved our lives in 2025 We may earn revenue from the products available on this page and participate in affiliate programs. When you live with small annoyances, frustration can build over time. You can only catch your belt loop on a drawer handle so many times before you hit your limit. Several of this year's home innovations address those seemingly small hurdles that can make a big difference in your home life. The monthly chore of replacing an air filter and the seemingly simple task of finding a place to store the lawn mower when not in use get clever solutions.

Transfer Learning (TL) is currently the most effective approach for modeling building thermal dynamics when only limited data are available. TL uses a pretrained model that is fine-tuned to a specific target building. However, it remains unclear how to proceed after initial fine-tuning, as more operational measurement data are collected over time. This challenge becomes even more complex when the dynamics of the building change, for example, after a retrofit or a change in occupancy. In Machine Learning literature, Continual Learning (CL) methods are used to update models of changing systems. TL approaches can also address this challenge by reusing the pretrained model at each update step and fine-tuning it with new measurement data. A comprehensive study on how to incorporate new measurement data over time to improve prediction accuracy and address the challenges of concept drifts (changes in dynamics) for building thermal dynamics is still missing. Therefore, this study compares several CL and TL strategies, as well as a model trained from scratch, for thermal dynamics modeling during building operation. The methods are evaluated using 5--7 years of simulated data representative of single-family houses in Central Europe, including scenarios with concept drifts from retrofits and changes in occupancy. We propose a CL strategy (Seasonal Memory Learning) that provides greater accuracy improvements than existing CL and TL methods, while maintaining low computational effort. SML outperformed the benchmark of initial fine-tuning by 28.1\% without concept drifts and 34.9\% with concept drifts.

Improving energy efficiency in residential buildings is critical to combating climate change and reducing greenhouse gas emissions. Retrofitting existing buildings, which contribute a significant share of energy use, is therefore a key priority, especially in regions with outdated building stock. Artificial Intelligence (AI) and Machine Learning (ML) can automate retrofit decision-making and find retrofit strategies. However, their use faces challenges of data availability, model transparency, and compliance with national and EU AI regulations including the AI act, ethics guidelines and the ALTAI. This paper presents a trustworthy-by-design ML-based decision support framework that recommends energy efficiency strategies for residential buildings using minimal user-accessible inputs. The framework merges Conditional Tabular Generative Adversarial Networks (CTGAN) to augment limited and imbalanced data with a neural network-based multi-label classifier that predicts potential combinations of retrofit actions. To support explanation and trustworthiness, an Explainable AI (XAI) layer using SHapley Additive exPlanations (SHAP) clarifies the rationale behind recommendations and guides feature engineering. Two case studies validate performance and generalization: the first leveraging a well-established, large EPC dataset for England and Wales; the second using a small, imbalanced post-retrofit dataset from Latvia (RETROFIT-LAT). Results show that the framework can handle diverse data conditions and improve performance up to 53% compared to the baseline. Overall, the proposed framework provides a feasible, interpretable, and trustworthy AI system for building retrofit decision support through assured performance, usability, and transparency to aid stakeholders in prioritizing effective energy investments and support regulation-compliant, data-driven innovation in sustainable energy transition.

Transfer learning (TL) can improve data-driven modeling of building thermal dynamics. Therefore, many new TL research areas emerge in the field, such as selecting the right source model for TL. However, these research directions require massive amounts of thermal building data which is lacking presently. Neither public datasets nor existing data generators meet the needs of TL research in terms of data quality and quantity. Moreover, existing data generation approaches typically require expert knowledge in building simulation. We present BuilDa, a thermal building data generation framework for producing synthetic data of adequate quality and quantity for TL research. The framework does not require profound building simulation knowledge to generate large volumes of data. BuilDa uses a single-zone Modelica model that is exported as a Functional Mock-up Unit (FMU) and simulated in Python. We demonstrate BuilDa by generating data and utilizing it for pretraining and fine-tuning TL models.

Heating, Ventilation, and Air-Conditioning (HVAC) systems account for a substantial share of global building energy use, making reliable anomaly detection essential for improving efficiency and reducing emissions. Classical rule-based approaches offer explainability but lack adaptability, while deep learning methods provide predictive power at the cost of transparency, efficiency, and physical plausibility. Recent attempts to use Large Language Models (LLMs) for anomaly detection improve interpretability but largely ignore the physical principles that govern HVAC operations. We present PILLM, a Physics-Informed LLM framework that operates within an evolutionary loop to automatically generate, evaluate, and refine anomaly detection rules. Our approach introduces physics-informed reflection and crossover operators that embed thermodynamic and control-theoretic constraints, enabling rules that are both adaptive and physically grounded. Experiments on the public Building Fault Detection dataset show that PILLM achieves state-of-the-art performance while producing diagnostic rules that are interpretable and actionable, advancing trustworthy and deployable AI for smart building systems.

Dynamic energy systems and controls require advanced modeling frameworks to design and test supervisory and fault tolerant strategies. Modelica is a widely used equation based language, but developing control modules is labor intensive and requires specialized expertise. This paper examines the use of large language models (LLMs) to automate the generation of Control Description Language modules in the Building Modelica Library as a case study. We developed a structured workflow that combines standardized prompt scaffolds, library aware grounding, automated compilation with OpenModelica, and human in the loop evaluation. Experiments were carried out on four basic logic tasks (And, Or, Not, and Switch) and five control modules (chiller enable/disable, bypass valve control, cooling tower fan speed, plant requests, and relief damper control). The results showed that GPT 4o failed to produce executable Modelica code in zero shot mode, while Claude Sonnet 4 achieved up to full success for basic logic blocks with carefully engineered prompts. For control modules, success rates reached 83 percent, and failed outputs required medium level human repair (estimated one to eight hours). Retrieval augmented generation often produced mismatches in module selection (for example, And retrieved as Or), while a deterministic hard rule search strategy avoided these errors. Human evaluation also outperformed AI evaluation, since current LLMs cannot assess simulation results or validate behavioral correctness. Despite these limitations, the LLM assisted workflow reduced the average development time from 10 to 20 hours down to 4 to 6 hours per module, corresponding to 40 to 60 percent time savings. These results highlight both the potential and current limitations of LLM assisted Modelica generation, and point to future research in pre simulation validation, stronger grounding, and closed loop evaluation.

Computational models have emerged as powerful tools for energy modeling research, touting scalability and quantitative results. However, these models require a plethora of data, some of which is inaccessible, expensive, or raises privacy concerns. We introduce a modular multimodal framework to produce this data from publicly accessible residential information and images using generative artificial intelligence (AI). Additionally, we provide a pipeline demonstrating this framework, and we evaluate its generative AI components. Our experiments show that our framework's use of AI avoids common issues with generative models. Our framework produces realistic, labeled data. By reducing dependence on costly or restricted data sources, we pave a path towards more accessible and reproducible research.

Reproducibility is a cornerstone of scientific research, enabling independent verification and validation of empirical findings. The topic gained prominence in fields such as psychology and medicine, where concerns about non - replicable results sparked ongoing discussions about research practices. In recent years, the fast-growing field of Machine Learning (ML) has become part of this discourse, as it faces similar concerns about transparency and reliability. Some reproducibility issues in ML research are shared with other fields, such as limited access to data and missing methodological details. In addition, ML introduces specific challenges, including inherent nondeterminism and computational constraints. While reproducibility issues are increasingly recognized by the ML community and its major conferences, less is known about how these challenges manifest in applied disciplines. This paper contributes to closing this gap by analyzing the transparency and reproducibility standards of ML applications in building energy systems. The results indicate that nearly all articles are not reproducible due to insufficient disclosure across key dimensions of reproducibility. 72% of the articles do not specify whether the dataset used is public, proprietary, or commercially available. Only two papers share a link to their code - one of which was broken. Two-thirds of the publications were authored exclusively by academic researchers, yet no significant differences in reproducibility were observed compared to publications with industry-affiliated authors. These findings highlight the need for targeted interventions, including reproducibility guidelines, training for researchers, and policies by journals and conferences that promote transparency and reproducibility.

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Fox News senior national correspondent William La Jeunesse reports on proposed changes to California's electric bills on'Special Report.' Power saver programs are utility-sponsored demand response initiatives that help reduce electricity usage during periods of peak demand. These programs typically target central air conditioners (AC) and heat pumps, since cooling equipment drives spikes in summer energy demand. In exchange for incentives such as bill credits or rebates, participating homeowners allow the utility to temporarily adjust or cycle their HVAC systems on hot days. I recently received an email from Leah, an HVAC professional based in Rio Rancho, New Mexico.