
After the questioning of the competence of diagnosticians (7 out of 10 DPEs were incorrect), thousands or even millions of homes wrongly classified as thermal sieves, it is now a massive "fraud" estimated at nearly 20 billion euros that is being denounced. These figures highlight the urgency of improving the system to restore confidence and guarantee more accurate assessments. Diagnosticians, often deprived of complete and precise information, are sometimes forced to fill in data by default, which harms the accuracy of the diagnoses.
Why is the DPE criticized?
The DPE is criticized for several major reasons related to the data it uses. First of all, the quality of the data collected is often insufficient or imprecise, with diagnosticians having to provide a lot of information that is sometimes unavailable or approximate. This leads to inconsistent results and variable reliability of diagnostics. Furthermore, standardized calculation methods do not always reflect the reality of uses, such as the behavior of occupants or the particularities of buildings. Finally, complacent DPEs, where information is deliberately distorted, increase distrust of this tool, which is nevertheless crucial for the energy transition.
Several levers to maximize the impact of the DPE:
Make all DPE data accessible and pre-fill diagnostics
Opening up DPE data in Open Data is a first step, but we need to go further. Combined with the integration of information from the Carnet d'Information du Logement (CIL), it would allow a large part of the data required for diagnosis to be automatically pre-filled. By using these reliable and up-to-date databases, diagnosticians would save valuable time, up to half of their processing time, according to our estimates. This time saving would allow them to focus on verifying and checking input information rather than on manual data collection. This would improve the quality of diagnoses while reducing the risk of human error and increasing the efficiency of the process.
Driving innovation through data
The opening of DPE data and the integration of CIL information can serve as a catalyst for innovation. By enriching this data with technological solutions, private actors could develop suitable tools, such as real-time control platforms, energy simulators, or algorithms for the continuing training of diagnosticians. This would strengthen the ecosystem by further involving private actors and ensuring better control of the quality of diagnostics.
Using artificial intelligence to process this data
Artificial intelligence (AI) offers promising prospects for improving the accuracy and speed of diagnostics. By using algorithms for processing data from files, APIs or the web, it would be possible to automate a large part of repetitive tasks and identify inconsistencies in data in real time. These solutions would also make it possible to provide predictive analyses, for example by simulating the impacts of different energy renovations on housing performance. Integrating AI into the process would not only strengthen the reliability of DPEs, but would also offer personalized tools to support individuals and professionals in their decisions.
Promoting serious diagnosticians
The energy diagnostics sector is often marked by a spiral of low prices, sometimes to the detriment of quality. To restore credibility to the DPE, it is essential to promote competent professionals. This could be done by introducing quality criteria such as diagnostic accuracy, ongoing training or customer feedback. These actions will strengthen the reliability of assessments and will allow diagnosticians to have a more rigorous and rewarding framework.
Engaging individuals as actors in the transition
In addition, it would be relevant to encourage individuals to pre-fill some of the information required for the DPE, based on the data already available in the CIL. This could be facilitated by a simple interface, with the prior sending of documents and photos, allowing them to actively contribute and make the diagnoses even more precise and complete. By making individuals responsible, we could not only improve the quality of the diagnoses, but also strengthen their involvement in the energy transition.
The DPE: A reinforced lever for the energy transition
By integrating tools like the CIL, which provides reliable historical and technical data, we are strengthening the effectiveness of the DPE. This improves decision-making in energy renovation projects and provides owners, tenants and buyers with more transparent and reliable information. A DPE well-powered by accurate data helps restore confidence and facilitate the energy transition.
Why act now?
The status quo is holding back energy renovation and fueling citizens' doubts. By opening up data, promoting competent diagnosticians and integrating tools such as the CIL, we can transform the DPE into a strategic lever to achieve our climate objectives.
Tribune by Éric Houdet, founder of Homapi (LinkedIn).