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Predicting residential electricity consumption using aerial and street view images - ScienceDirect
Source: sciencedirect.com
Article summary:
1. This article presents an innovative approach to modeling building electricity consumption that relies solely on publicly available aerial and street view images.
2. The results show that the model can predict electricity consumption about as well as conventional models, which are trained on commonly used features.
3. Spatially aggregating the predictions further improves the results of the model.
Article analysis:
The article is generally trustworthy and reliable, as it provides a detailed overview of an innovative approach to modeling building electricity consumption using aerial and street view images. The authors provide evidence for their claims by comparing their model to state-of-the-art benchmark models from recent literature, which rely on building data that is mostly private and difficult to obtain at a large scale. Furthermore, they demonstrate that spatially aggregating the predictions further improves the results of their model.
The article does not appear to have any major biases or one-sided reporting, as it provides a comprehensive overview of the research conducted and its findings. It also does not contain any unsupported claims or missing points of consideration, as all claims are backed up with evidence from relevant literature and case studies. Additionally, there is no promotional content or partiality in the article, as it objectively presents both sides of the argument without favoring either one over the other. Lastly, possible risks associated with this approach are noted in the article, such as data privacy concerns due to relying solely on publicly available images for modeling electricity consumption.