Enhancing household-level load forecasts using daily load profile clustering

Forecasting the electricity demand for individual households is important for both consumers and utilities due to the increasing decentralized nature of the electricity system. Particularly, utilities often have very little information about their consumers except for aggregate building level loads, without knowledge of interior details about the household appliance sets or occupants. In this paper, we explore the possibility of enhancing the day-ahead load forecasts for hundreds of individual households by clustering their daily load profile history to obtain each consumer's specific typical consumption patterns. The clustering method is based on load profile shape using the Earth Mover's Distance metric to calculate similarity between load profiles. The forecasting methods then predict the next day shape from the empirical probability of previous cluster transitions in the consumer's load history and estimate the magnitude either by using historical load relationships with temperature and forecast temperatures or previous day consumption levels. The generated forecasts are compared to a benchmark Multiple Linear Regression (MLR) day-ahead forecast and persistence forecasts for all individuals. While at the aggregate level the MLR method represents a significant improvement over persistence forecasts, on an individual level we find that the best forecasting model is specific to the individual. In particular, we find that the MLR model produces lower errors when consumers have a consistent daily temperature response and the cluster model with previous day magnitude produces lower errors for consumers whose consumption changes abruptly in magnitude for several days at a time. Our work adds to the state of knowledge surrounding individual household load forecasting and demonstrates the potential for cluster-based methodologies to enhance short term load forecasts

DyMo: Dynamic Monitoring of Large Scale LTE-Multicast Systems

LTE evolved Multimedia Broadcast/Multicast Service (eMBMS) is an attractive solution for video delivery to very large groups in crowded venues. However, deployment and management of eMBMS systems is challenging, due to the lack of realtime feedback from the User Equipment (UEs). Therefore, we present the Dynamic Monitoring (DyMo) system for low-overhead feedback collection. DyMo leverages eMBMS for broadcasting Stochastic Group Instructions to all UEs. These instructions indicate the reporting rates as a function of the observed Quality of Service (QoS). This simple feedback mechanism collects very limited QoS reports from the UEs. The reports are used for network optimization, thereby ensuring high QoS to the UEs. We present the design aspects of DyMo and evaluate its performance analytically and via extensive simulations. Specifically, we show that DyMo infers the optimal eMBMS settings with extremely low overhead, while meeting strict QoS requirements under different UE mobility patterns and presence of network component failures. For instance, DyMo can detect the eMBMS Signal-to-Noise Ratio (SNR) experienced by the 0.1% percentile of the UEs with Root Mean Square Error (RMSE) of 0.05% with only 5 to 10 reports per second regardless of the number of UEs

Sparse kernel SVMs via cutting-plane training

Appeal from a decision of the Board of Review, Industrial Commission of Utah on February 7, 1989

Linear electric generation system to harvest vibration energy from a running vehicle

Methods to harvest energy that is wasted during road vehicle operation have been studied in recent years. Mechanical vibrations from vertical motion induced by road bumps dissipate a large amount of energy. A linear electric generation system is presented to harvest this energy. The system uses mechanical resonance to maximize the efficiency of harvesting. A shock absorber suspension and electric generator mathematical model were created to analyze the vibration characteristics induced by road bumps during vehicle operation. An electromagnetic simulation using the commercial software MAXWELL (Ver. 13, ANSOFT, USA) was performed to predict the electricity generation. Finally, the magnetic circuit design was optimized to improve the amount of electricity generated. The results demonstrate the possibility of using the proposed approach in practical applications

Biorefining Waste Sludge From Water and Sewage Treatment Plants Into Eco-Construction Material

This study aims to investigate the feasibility of using different waste sludge and coal combustion residuals in eco-concrete block production. The compressive strength of the eco-concrete blocks produced by waterworks sludge, bottom and fly ashes were 36 MPa, which comply with the standard specifications for paving blocks in Hong Kong. The optimal mixing proportion (by weight) of different materials in the blocks, such as aggregates, cementitious materials, water, and fly ash was 1.1:1.0:0.5:0.22, respectively. The environmental and toxicological impacts of the final products were then evaluated according to the toxicity characteristic leaching procedure (TCLP). While several heavy metals (i.e., Hg, Cu, and Pb) have been identified in the specimens, the levels of these contaminants complied with Standards (US 40 CFR 268.48). Waste materials generated from water and sewage treatment processes and power plants are feasible to be used as ingredients for paving concrete block production. These products are environmentally acceptable and mechanically suitable for resource recovery of waste materials

Blood-Based Biomarkers of Aggressive Prostate Cancer

Purpose: Prostate cancer is a bimodal disease with aggressive and indolent forms. Current prostate-specific-antigen testing and digital rectal examination screening provide ambiguous results leading to both under-and over-treatment. Accurate, consistent diagnosis is crucial to risk-stratify patients and facilitate clinical decision making as to treatment versus active surveillance. Diagnosis is currently achieved by needle biopsy, a painful procedure. Thus, there is a clinical need for a minimally-invasive test to determine prostate cancer aggressiveness. A blood sample to predict Gleason score, which is known to reflect aggressiveness of the cancer, could serve as such a test. Materials and Methods: Blood mRNA was isolated from North American and Malaysian prostate cancer patients/controls. Microarray analysis was conducted utilizing the Affymetrix U133 plus 2·0 platform. Expression profiles from 255 patients/controls generated 85 candidate biomarkers. Following quantitative real-time PCR (qRT-PCR) analysis, ten disease-associated biomarkers remained for paired statistical analysis and normalization. Results: Microarray analysis was conducted to identify 85 genes differentially expressed between aggressive prostate cancer (Gleason score ≥8) and controls. Expression of these genes was qRT-PCR verified. Statistical analysis yielded a final seven-gene panel evaluated as six gene-ratio duplexes. This molecular signature predicted as aggressive (ie, Gleason score ≥8) 55% of G6 samples, 49% of G7(3+4), 79% of G7(4+3) and 83% of G8-10, while rejecting 98% of controls. Conclusion: In this study, we have developed a novel, blood-based biomarker panel which can be used as the basis of a simple blood test to identify men with aggressive prostate cancer and thereby reduce the overdiagnosis and overtreatment that currently results from diagnosis using PSA alone. We discuss possible clinical uses of the panel to identify men more likely to benefit from biopsy and immediate therapy versus those more suited to an “active surveillance” strategy

Identification of a novel Shank2 transcriptional variant in Shank2 knockout mouse model of autism spectrum disorder

Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders that are highly heterogeneous in clinical symptoms as well as etiologies. Mutations in SHANK2 are associated with ASD and accordingly, Shank2 knockout mouse shows ASD-like behavioral phenotypes, including social deficits. Intriguingly, two lines of Shank2 knockout (KO) mouse generated by deleting different exons (exon 6–7 or exon 7) showed distinct cellular phenotypes. Previously, we compared gene expressions between Shank2 KOs lacking exon 6–7 (e6–7 KO) and KOs lacking exon 7 (e7 KO) by performing RNA-seq. In this study, we expanded transcriptomic analyses to identify novel transcriptional variants in the KO mice. We found prominent expression of a novel exon (exon 4′ or e4) between the existing exons 4 and 5 in the Shank2 e6–7 KO model. Expression of the transcriptional variant harboring this novel exon was confirmed by RT-PCR and western blotting. These findings suggest that the novel variant may function as a modifier gene, which contributes to the differences between the two Shank2 mutant lines. Furthermore, our result further represents an example of genetic compensation that may lead to phenotypic heterogeneity among ASD patients with mutations in the same gene.This work was supported by the National Honor Scientist Program (NRF2012R1A3A1050385) through a grant to B.-K.K.; NRF-2017M3C7A1026959 to Y.-S.L.; NRF-2018H1A2A1061381 to G.P.; NRF-2017M3C9A6047623 to J.-H.L