The key challenge for household load forecasting lies in the high volatility and uncertainty of profiles.Traditional methods tend to avoid such by aggregation (to offset uncertainties), customer classification cluster uncertainties) spectral analysis filter out uncertainties).This paper, first time, aims directly learn applying a new breed machine learning algorithms -deep learning.However simply adding layers neural networks will cap performance due occurrence overfitting.A novel...

Deregulation exposes the inherent volatility of electricity price. Accurate price forecasting (EPF) could help market participants to hedge against movements and maximise their profits. The existing methods have limited capability integrating other external factors into model, such as weather, consumption natural gas This study proposes a deep recurrent neural network (DRNN) method forecast day-ahead in deregulated explore complex dependence structure multivariate EPF model. proposed can...

Currently, small object detection in complex remote sensing environments faces significant challenges. The detectors designed for this scenario have limitations, such as insufficient extraction of spatial local information, inflexible feature fusion, and limited global acquisition capability. In addition, there is a need to balance performance complexity when improving the model. To address these issues, paper proposes an efficient lightweight SCM-YOLO detector improved from YOLOv5 with...

Unmanned aerial vehicle (UAV) targets are typically small in size, occupy only a limited pixel area, and often located complex environments. Existing models, however, tend to overlook smaller backgrounds, making it easy miss important information resulting missing targets. This paper proposes an innovative UAV detection method called BRA-YOLOv10. Firstly, Bi-Level Routing Attention (BRA) is used during the feature extraction stage effectively reduce background interference. By focusing on...

Deep learning has been proven of great potential in various time-series forecasting applications. To exploit the and extendibility deep electricity load forecasting, this paper for first time presents a comprehensive assessment on performing at different levels through power systems. The is demonstrated via two extreme cases: 1) regional aggregated demand with an example New England data, 2) disaggregated household examples 100 individual households from Ireland. state-of-the-art recurrent...

Phase imbalance in the U.K. and European low-voltage (415 V, LV) distribution networks causes additional energy losses. A key barrier against understanding imbalance-induced losses is absence of high-resolution time-series data for LV networks. It remains a challenge to estimate that only have yearly average currents three phases. To address this insufficient challenge, paper proposes new customized statistical approach, named as clustering, classification, range estimation (CCRE) approach....

In this paper, typical strengths, fault levels, and source impedances are thoroughly analyzed calculated for the study of quality supply in 230/400 V 50 Hz distribution systems. Considering all disparity network design, is based on a comprehensive database containing arrangements equipment U.K./European systems, as well fully documented generic models supplying four residential load subsectors U.K., i.e., from metropolitan to rural areas. Thus, paper proposes an alternative method...

Uncertainty has become a key challenge in energy profiles at the domestic level, which is influenced by variety of factors, such as behaviors, technologies, weather conditions, prices, and so on. These factors influence household demand various time horizons, hence result diverse uncertainty natures across time-scales. Therefore, it crucial to understand temporal different time-scales, particularly intra-day inter-days. This paper first attempts quantify characterize uncertainties...

Background This retrospective study investigated the effect of nicotine dependence on required postoperative opioid administration in patients undergoing thoracic surgery. Methods The subjects consisted 215 male (112 nonsmokers, 103 smokers) who underwent surgery and received patient‐controlled intravenous analgesia. Evaluations were based results F agerstrom T est N icotine D ependence ( FTND ) questionnaires. Smokers categorized as low‐nicotine dependent LD (n = 58) or highly‐nicotine HD ,...

Demand side response (DSR) is expected to bring benefits in lowering or deferring energy costs, network infrastructure investment and reducing customers' bills. However, very little evidence suggest DSR can perform as expected. Therefore, this paper for the first time assesses differences between realistic performance domestic customers. In detail, it compares load profiles typical used by industry. Then, performances are examined using in-home battery, time-of-use tariffs management system....

Previous pricing strategies including time-of-use price and dynamic reflect system marginal cost calculate consumers' bills according to the quantity of their electricity usage. Little effort is made understand impact power volatility on total production costs. This paper thus proposes a novel strategy reflecting arising from volatility. Firstly, investigated quantify cost. Secondly, model proposed allocate consumers renewable energy generations (REGs). It can reveal coupling relationship...

Intel's Vector Neural Network Instruction (VNNI) provides higher efficiency on calculating dense linear algebra (DLA) computations than conventional SIMD instructions. However, existing auto-vectorizers frequently deliver suboptimal utilization of VNNI by either failing to recognize VNNI's unique computation pattern at the innermost loops/basic blocks, or producing inferior code through constrained and rudimentary peephole optimizations/pattern matching techniques. Auto-tuning frameworks...

Given the tendency of increasingly heterogeneous AI systems and large workload scale deep neural networks (DNNs), there is an urgent demand for model scheduling to improve execution performance in computational systems. However, this very challenging because task under high-dimensional search space NP-hard problem. Existing works either schedule naive spaces without simplifications or oversimplifies optimisation, which hard strike a balance between efficiency optimality.

The distribution system has become highly uncertain due to the changing landscape. This paper proposes a novel probabilistic network pricing method based on Long-Run-Incremental Pricing (LRIC) considering demand uncertainty. probability distributions of annual peak and its coincidence are represented by random variables following certain distributions. Probabilistic power flow is formulated convolution nodal density function (PDF). LRIC then applied calculate charges combining Tail value at...