Big data has potential to unlock novel groundbreaking opportunities in power grid that enhances a multitude of technical, social, and economic gains. As technologies evolve conjunction with measurement communication technologies, this results unprecedented amount heterogeneous big data. In particular, computational complexity, security, operational integration into system planning frameworks are the key challenges transform large dataset actionable outcomes. context, suitable analytics...

Chimeric antigen receptor (CAR) T-cell therapy plays a critical role in the treatment of B-cell hematologic malignancies. The combination PD-1 inhibitors and CAR-T has shown encouraging results treating patients with relapsed/refractory (R/R) diffuse large lymphoma (DLBCL). However, there are still cases where is ineffective. This study aimed to investigate IL4I1 poor efficacy CD19 combined R/R DLBCL explore potential mechanisms. Transcriptomic metabolomic correlation analyses were performed...

Solar energy penetration both at utility scale and residential has been increasing an exponential rate. However, its stochastic nature poses great challenge to power grid operation. Knowing how much solar generation in advance is vital for balancing, planning optimization. Therefore, forecast essential the stability operation efficiency of today's smart grid. Although sun path can be computed with physical laws, prediction production remains very challenging problem field simulation...

Buildings consume about 40% of the total energy in most countries contributing to a significant amount greenhouse gas (GHG) emissions and global warming. Therefore, reducing consumption buildings, making buildings more efficient operating manner are important tasks today's world. Analytics can play an role identifying saving opportunities by modeling analyzing how is consumed buildings. In this paper, set analytics which assist building owners, facility managers, operators tenants assessing,...

Accurately and globally mapping human infrastructure is an important challenging task with applications in routing, regulation compliance monitoring, natural disaster response management etc.. In this paper we present progress developing algorithmic pipeline distributed compute system that automates the process of map creation using high resolution aerial images. Unlike previous studies, most which use datasets are available only a few cities across world, utilizes publicly imagery data,...

The energy load data in the micro-energy network are a time series with sequential and nonlinear characteristics. This paper proposes model based on encode-decode architecture ConvLSTM for multi-scale prediction of multi-energy loads network. We apply ConvLSTM, LSTM, attention mechanism multi-task learning concepts to construct specifically processing forecasting In this paper, is used encode input series. assign different weights features, which subsequently decoded by decoder LSTM layer....

How can independent system operators (ISOs) take advantage of probabilistic solar forecasts to lower generation costs and improve reliability power systems? We discuss one three-step approach for doing so, focusing on how such might help the California Independent System Operator (CAISO) prepare unexpected net load ramps, where equals gross demand minus wind production. First, we enhance an existing forecasting provide well-calibrated hours-ahead forecasts. then relate degree uncertainty...

Non-Normal demand is the with infrequent occurrences or irregular sizes, which very difficult to forecast. In this study, an ensemble empirical mode decomposition (EEMD) based support vector machines (SVMs) learning approach proposed forecast in these two cases. This under a “decomposition-and-ensemble” principal decompose original non-normal series into several independent “smooth” and “continuous” subseries including small number of intrinsic functions (IMFs) residue by EEMD technique....

As a clean and renewable energy source, wind has been increasingly gaining global attention. Wind speed forecasting is of great significance for domain: planning design farms, farm operation control, power prediction, grid scheduling, more. Many algorithms have proposed to improve forecast accuracy. In terms such factors, in this article, novel hybrid method was based on Kalman filter Generalized regression neural network as well the idea filtering error threshold data preprocessing. The...

In the United States, buildings sector accounted for about 41% of primary energy consumption in 2010, which was around 44% more than transportation and 36% industrial sector. Real time forecasts building using weather are crucial effective management. And Variable Base Degree Day (VBDD) model has been proven this field. VBDD model, two factors mainly determine accuracy forecasts, where first is computation optimal base values dynamic second forecasts. paper, we motivated by field study...

Due to the stochastic nature of occupants' behaviors, forecasting individual household-level residential load has been a challenging problem. In this paper, by correlating habitual electricity usages and short-term variances, new prediction intervals (PIs) based method combining deep learning error ranges estimation is proposed. Firstly, double-layer Long Short-Time Memory (LSTM) model constructed predict hourly demand at household-level. The statistical errors analysis shows that LSTM have...

Reducing energy consumption, improving efficiency, and reducing greenhouse gas (GHG) emissions are among the most important initiatives in today's world. Occupied buildings consume a substantial amount of energy, mounting to about 40% overall consumption countries. The majority world's population either lives or works buildings; therefore, everybody can contribute controlling GHG emissions, mitigating climate change its potential impact. We developed an analytical tool that assist building...

<title>Abstract</title> Chimeric antigen receptor (CAR) T-cell therapy plays a critical role in the treatment of B-cell hematologic malignancies. The combination PD-1 inhibitors and CAR-T has shown encouraging results treating patients with relapsed/refractory (R/R) diffuse large lymphoma (DLBCL). However, there are still cases where is ineffective. This study aimed to investigate IL4I1 poor efficacy CD19 combined R/R DLBCL explore potential mechanisms. Transcriptomic metabolomic correlation...

The accurate prediction of power load is great significance for the safe operation smart grid and transactions market participants since data series exhibits nonlinearity volatility. In this paper, a new hybrid approach deterministic short-term forecasting proposed based on wavelet transform deep policy gradient. approach, original sequence first decomposed by into some sub-frequency sequences, each can have better outlines behavior. A gradient model then employed to extract nonlinear...

Abstract With the development of social resources, people's consumption energy is huge, so renewable energy, such as wind has been widely concerned and developed. Although there sufficient power generation, its output some problems uncertainty, which leads to insufficient utilization resources uneven quality level, brings great challenges grid connection. To solve this problem, a short‐term prediction model combining firefly algorithm long term memory network proposed. The main motivation...

To maximize the expected profits and manage risks of renewable energy system under electricity market environment, scenario-based- stochastic optimization model can be established to generate bidding strategies, in which probabilistic scenarios risk parameters are usually obtained by using statistical or machine learning methods. This paper proposes a practical multivariate method for parameter scenario generation, is used wind faced with uncertain prices power productions, it considers...

Wordle is a daily word guessing game where players aim to guess five-letter in six or fewer attempts. To predict user participation, this study establishes SIRS model explain and the quantity of reported results, based on an analysis text propagation mechanism. player group divided into entertainment strategic players. An Analog Simulation Model established simulate word-filling process these two types players, thereby obtaining result distribution for Subsequently, Bayesian formula used...

Accurately and globally mapping human infrastructure is an important challenging task with applications in routing, regulation compliance monitoring, natural disaster response management etc.. In this paper we present progress developing algorithmic pipeline distributed compute system that automates the process of map creation using high resolution aerial images. Unlike previous studies, most which use datasets are available only a few cities across world, utilizes publicly imagery data,...

research-article Share on The Portfolio Model Based Temporal Convolution Networks and the Empirical Research Chinese Stock Market Authors: Rui Zhang Beijing Jiaotong University, China ChinaView Profile , Zuoquan Marui Du Xiaomin Wang Authors Info & Claims CSAI 2021: 2021 5th International Conference Computer Science Artificial IntelligenceDecember Pages 290–295https://doi.org/10.1145/3507548.3507593Online:09 March 2022Publication History 0citation11DownloadsMetricsTotal Citations0Total...