Load prediction with an improved feature selection method for building energy management of an office park

Load prediction plays a significant role in building energy management. An accurate HVAC load prediction model highly depends on the feature selection and the quality of training data. In previous work on load prediction, the input features are majorly manually selected by expertise, which is relatively subjective and lacks theoretical supports. Using the real building operational data collected from an office park located in Hangzhou, this paper developed a short-term cooling load prediction model, in which the input features are selected based on an analysis on the heat transfer process. Combined with qualitative analysis of the real data, several features such as outdoor air enthalpy and indoor black-bulb temperatures from different orientations are introduced into the model. The proposed model was then applied to the HVAC control system of the office park. Compared to the load prediction model with commonly used features, the proposed model reduced CRVMSE by 21% and MAPE by 30% during the operation period of the system. Furthermore, the impacts of training dataset size and prediction time range on model’s accuracy and training time were discussed

Fast Model Debias with Machine Unlearning

Recent discoveries have revealed that deep neural networks might behave in a biased manner in many real-world scenarios. For instance, deep networks trained on a large-scale face recognition dataset CelebA tend to predict blonde hair for females and black hair for males. Such biases not only jeopardize the robustness of models but also perpetuate and amplify social biases, which is especially concerning for automated decision-making processes in healthcare, recruitment, etc., as they could exacerbate unfair economic and social inequalities among different groups. Existing debiasing methods suffer from high costs in bias labeling or model re-training, while also exhibiting a deficiency in terms of elucidating the origins of biases within the model. To this respect, we propose a fast model debiasing framework (FMD) which offers an efficient approach to identify, evaluate and remove biases inherent in trained models. The FMD identifies biased attributes through an explicit counterfactual concept and quantifies the influence of data samples with influence functions. Moreover, we design a machine unlearning-based strategy to efficiently and effectively remove the bias in a trained model with a small counterfactual dataset. Experiments on the Colored MNIST, CelebA, and Adult Income datasets along with experiments with large language models demonstrate that our method achieves superior or competing accuracies compared with state-of-the-art methods while attaining significantly fewer biases and requiring much less debiasing cost. Notably, our method requires only a small external dataset and updating a minimal amount of model parameters, without the requirement of access to training data that may be too large or unavailable in practice

Impact of emission controls on air quality in Beijing during APEC 2014: implications from water-soluble ions and carbonaceous aerosol in PM2.5 and their precursors

Stringent emission controls during the Asia Pacific Economic Cooperation Summit (APEC; November 5–11, 2014) provide a valuable opportunity to examine the impact of such measures on the chemical properties of PM2.5 and other air pollutants. Here, we measured the water-soluble inorganic ions (WSII) and carbonaceous species in PM2.5, NH3 and NO2 at multiple sites in Beijing between September and November 2014. Relative to the pre-APEC period (September and October 2014), significant reductions in the average concentrations of WSII (69% for NO3−, 68% for SO42−, 78% for NH4+, and 29–71% for other species), elemental carbon (EC, 43%) and organic carbon (OC, 45%) in PM2.5 were found during the APEC period. The contributions of secondary inorganic ions (SIA, including SO42−, NO3−, and NH4+) to PM2.5 were significantly lower during the APEC period (9–44%), indicating a combination of lower gaseous precursor emissions and a relative weak secondary aerosol formation. Ion-balance calculations indicated that the PM2.5 sample in the pre-APEC period was alkaline but was acidic during the APEC period. Relatively lower mean concentrations of EC (1.5 μg m−3), OC (10.5 μg m−3), secondary organic carbon (SOC, 3.3 μg m−3), secondary organic aerosol (SOA, 5.9 μg m−3) and primary organic aerosol (POA, 10.0 μg m−3) appeared during the APEC period. The average concentrations of NH3 and NO2 at all road sites were significantly reduced by 48 and 60% during the APEC period, which is consistent with clear reductions in satellite NH3 columns over Beijing city in the same period. This finding suggests that reducing traffic emissions could be a feasible method to control urban NH3 pollution. During the APEC period, concentrations of PM2.5, PM10, NO2, SO2 and CO from the Beijing city monitoring network showed significant reductions at urban (20–60%) and rural (18–57%) sites, whereas O3 concentrations increased significantly (by 93% and 53%, respectively). The control measures taken in the APEC period substantially decreased PM2.5 pollution but can increase ground O3, which also merits attention

Assessing the Privacy Benefits of Domain Name Encryption

As Internet users have become more savvy about the potential for their Internet communication to be observed, the use of network traffic encryption technologies (e.g., HTTPS/TLS) is on the rise. However, even when encryption is enabled, users leak information about the domains they visit via DNS queries and via the Server Name Indication (SNI) extension of TLS. Two recent proposals to ameliorate this issue are DNS over HTTPS/TLS (DoH/DoT) and Encrypted SNI (ESNI). In this paper we aim to assess the privacy benefits of these proposals by considering the relationship between hostnames and IP addresses, the latter of which are still exposed. We perform DNS queries from nine vantage points around the globe to characterize this relationship. We quantify the privacy gain offered by ESNI for different hosting and CDN providers using two different metrics, the k-anonymity degree due to co-hosting and the dynamics of IP address changes. We find that 20% of the domains studied will not gain any privacy benefit since they have a one-to-one mapping between their hostname and IP address. On the other hand, 30% will gain a significant privacy benefit with a k value greater than 100, since these domains are co-hosted with more than 100 other domains. Domains whose visitors' privacy will meaningfully improve are far less popular, while for popular domains the benefit is not significant. Analyzing the dynamics of IP addresses of long-lived domains, we find that only 7.7% of them change their hosting IP addresses on a daily basis. We conclude by discussing potential approaches for website owners and hosting/CDN providers for maximizing the privacy benefits of ESNI.Comment: In Proceedings of the 15th ACM Asia Conference on Computer and Communications Security (ASIA CCS '20), October 5-9, 2020, Taipei, Taiwa

The large area detector onboard the eXTP mission

The Large Area Detector (LAD) is the high-throughput, spectral-timing instrument onboard the eXTP mission, a flagship mission of the Chinese Academy of Sciences and the China National Space Administration, with a large European participation coordinated by Italy and Spain. The eXTP mission is currently performing its phase B study, with a target launch at the end-2027. The eXTP scientific payload includes four instruments (SFA, PFA, LAD and WFM) offering unprecedented simultaneous wide-band X-ray timing and polarimetry sensitivity. The LAD instrument is based on the design originally proposed for the LOFT mission. It envisages a deployed 3.2 m2 effective area in the 2-30 keV energy range, achieved through the technology of the large-area Silicon Drift Detectors - offering a spectral resolution of up to 200 eV FWHM at 6 keV - and of capillary plate collimators - limiting the field of view to about 1 degree. In this paper we will provide an overview of the LAD instrument design, its current status of development and anticipated performance