scholarly journals The state-of-the-art of the short term hydro power planning with large amount of wind power in the system

2011 ◽  
Author(s):  
Yelena Vardanyan ◽  
Mikael Amelin
Keyword(s):  
Wind Power ◽  
State Of The Art ◽  
The State ◽  
Short Term ◽  
Hydro Power

scholarly journals Learning from Interventions Using Hierarchical Policies for Safe Learning

2020 ◽  
Vol 34 (06) ◽  
pp. 10352-10360
Author(s):  
Jing Bi ◽  
Vikas Dhiman ◽  
Tianyou Xiao ◽  
Chenliang Xu
Keyword(s):  
Reaction Time ◽  
State Of The Art ◽  
The State ◽  
Policy Framework ◽  
Asymptotic Performance ◽  
Short Term ◽  
Learning From Demonstrations ◽  
Hierarchical Levels ◽  
Long Term Behavior

Learning from Demonstrations (LfD) via Behavior Cloning (BC) works well on multiple complex tasks. However, a limitation of the typical LfD approach is that it requires expert demonstrations for all scenarios, including those in which the algorithm is already well-trained. The recently proposed Learning from Interventions (LfI) overcomes this limitation by using an expert overseer. The expert overseer only intervenes when it suspects that an unsafe action is about to be taken. Although LfI significantly improves over LfD, the state-of-the-art LfI fails to account for delay caused by the expert's reaction time and only learns short-term behavior. We address these limitations by 1) interpolating the expert's interventions back in time, and 2) by splitting the policy into two hierarchical levels, one that generates sub-goals for the future and another that generates actions to reach those desired sub-goals. This sub-goal prediction forces the algorithm to learn long-term behavior while also being robust to the expert's reaction time. Our experiments show that LfI using sub-goals in a hierarchical policy framework trains faster and achieves better asymptotic performance than typical LfD.

scholarly journals Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM

2019 ◽  
Vol 9 (20) ◽  
pp. 4237 ◽  
Author(s):  
Tuong Le ◽  
Minh Thanh Vo ◽  
Bay Vo ◽  
Eenjun Hwang ◽  
Seungmin Rho ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Performance Metrics ◽  
Short Term Memory ◽  
State Of The Art ◽  
Electric Energy ◽  
The State ◽  
Short Term ◽  
Electric Energy Consumption ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

The electric energy consumption prediction (EECP) is an essential and complex task in intelligent power management system. EECP plays a significant role in drawing up a national energy development policy. Therefore, this study proposes an Electric Energy Consumption Prediction model utilizing the combination of Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM) that is named EECP-CBL model to predict electric energy consumption. In this framework, two CNNs in the first module extract the important information from several variables in the individual household electric power consumption (IHEPC) dataset. Then, Bi-LSTM module with two Bi-LSTM layers uses the above information as well as the trends of time series in two directions including the forward and backward states to make predictions. The obtained values in the Bi-LSTM module will be passed to the last module that consists of two fully connected layers for finally predicting the electric energy consumption in the future. The experiments were conducted to compare the prediction performances of the proposed model and the state-of-the-art models for the IHEPC dataset with several variants. The experimental results indicate that EECP-CBL framework outperforms the state-of-the-art approaches in terms of several performance metrics for electric energy consumption prediction on several variations of IHEPC dataset in real-time, short-term, medium-term and long-term timespans.

“The Enthusiasm Expressed by Our Industry Friends”

Vulcan ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 100-124
Author(s):  
Adam Givens
Keyword(s):  
Long Range ◽  
State Of The Art ◽  
The State ◽  
Next Generation ◽  
Short Term ◽  
Us Army ◽  
The Face ◽  
Long Term Consequences ◽  
Fait Accompli

Abstract This article analyzes the groundbreaking 1952 plan by US Army leadership to develop a sizeable cargo helicopter program in the face of interservice opposition. It examines the influence that decision had in the next decade on the Army, the helicopter industry, and vtol technology. The Army’s procurement of large helicopters that could transport soldiers and materiel was neither a fait accompli nor based on short-term needs. Rather, archival records reveal that the decision was based on long-range concerns about the postwar health of the helicopter industry, developing the state of the art, and fostering new doctrinal concepts. The procurement had long-term consequences. Helicopters became central to Army war planning, and the ground service’s needs dictated the next generation of helicopter designs. That technology made possible the revolutionary airmobility concept that the Army took into Vietnam and also led to a flourishing commercial helicopter field.

scholarly journals Short Term Electric Load Forecasting Based on Data Transformation and Statistical Machine Learning

2020 ◽  
Vol 11 (1) ◽  
pp. 158
Author(s):  
Nikos Andriopoulos ◽  
Aristeidis Magklaras ◽  
Alexios Birbas ◽  
Alex Papalexopoulos ◽  
Christos Valouxis ◽  
...  
Keyword(s):  
Distribution System ◽  
Short Term Memory ◽  
State Of The Art ◽  
Load Forecasting ◽  
The State ◽  
Neural Nets ◽  
Interactive System ◽  
Electric Load ◽  
Short Term ◽  
Electric Load Forecasting

The continuous penetration of renewable energy resources (RES) into the energy mix and the transition of the traditional electric grid towards a more intelligent, flexible and interactive system, has brought electrical load forecasting to the foreground of smart grid planning and operation. Predicting the electric load is a challenging task due to its high volatility and uncertainty, either when it refers to the distribution system or to a single household. In this paper, a novel methodology is introduced which leverages the advantages of the state-of-the-art deep learning algorithms and specifically the Convolution Neural Nets (CNN). The main feature of the proposed methodology is the exploitation of the statistical properties of each time series dataset, so as to optimize the hyper-parameters of the neural network and in addition transform the given dataset into a form that allows maximum exploitation of the CNN algorithm’s advantages. The proposed algorithm is compared with the LSTM (Long Short Term Memory) technique which is the state of the art solution for electric load forecasting. The evaluation of the algorithms was conducted by employing three open-source, publicly available datasets. The experimental results show strong evidence of the effectiveness of the proposed methodology.

scholarly journals Transient events in bright debris discs: Collisional avalanches revisited

2018 ◽  
Vol 609 ◽  
pp. A98 ◽  
Author(s):  
P. Thebault ◽  
Q. Kral
Keyword(s):  
Optical Depth ◽  
State Of The Art ◽  
The State ◽  
Chain Reaction ◽  
Short Term ◽  
Self Consistent ◽  
Transient Events ◽  
Transient Structures ◽  
First Time ◽  
The Ideal

Context. A collisional avalanche is set off by the breakup of a large planetesimal, releasing vast amounts of small unbound grains that enter a debris disc located further away from the star, triggering there a collisional chain reaction that could potentially create detectable transient structures. Aims. We investigate this mechanism, using for the first time a fully self-consistent code coupling dynamical and collisional evolutions. We also quantify for the first time the photometric evolution of the system and investigate whether or not avalanches could explain the short-term luminosity variations recently observed in some extremely bright debris discs. Methods. We use the state-of-the-art LIDT-DD code. We consider an avalanche-favoring A6V star, and two set-ups: a “cold disc” case, with a dust release at 10 au and an outer disc extending from 50 to 120 au, and a “warm disc” case with the release at 1 au and a 5−12 au outer disc. We explore, in addition, two key parameters: the density (parameterized by its optical depth τ) of the main outer disc and the amount of dust released by the initial breakup. Results. We find that avalanches could leave detectable structures on resolved images, for both “cold” and “warm” disc cases, in discs with τ of a few 10-3, provided that large dust masses (≳1020−5 × 1022 g) are initially released. The integrated photometric excess due to an avalanche is relatively limited, less than 10% for these released dust masses, peaking in the λ ~ 10−20 μm domain and becoming insignificant beyond ~40–50 μm. Contrary to earlier studies, we do not obtain stronger avalanches when increasing τ to higher values. Likewise, we do not observe a significant luminosity deficit, as compared to the pre-avalanche level, after the passage of the avalanche. These two results concur to make avalanches an unlikely explanation for the sharp luminosity drops observed in some extremely bright debris discs. The ideal configuration for observing an avalanche would be a two-belt structure, with an inner belt (at ~1 or ~10 au for the “warm” and “cold” disc cases, respectively) of fractional luminosity f ≳ 10-4 where breakups of massive planetesimals occur, and a more massive outer belt, with τ of a few 10-3, into which the avalanche chain reaction develops and propagates.

scholarly journals The state of the nursing science in Spain

2002 ◽  
Vol 10 (2) ◽  
pp. 214-220 ◽  
Author(s):  
Ramón Camaño Puig
Keyword(s):  
Scientific Community ◽  
Historical Perspective ◽  
State Of The Art ◽  
The State ◽  
International Level ◽  
Short Term ◽  
Nursing Science ◽  
Negative Factors ◽  
Clear Description

This article is giving a historical perspective of the nursing science in Spain, comparing it with the situation of the science, and nursing science at international level. The author gave a very clear description of the state of the art, and the nursing outcome shared by Spanish authors with the rest of nursing scientific community, arriving to the conclusion that Spanish nursing is at the beginning of a process where, negative factors can be clearly identified and, potential measures to improve relatively easily nursing science can be taken in the short term.

scholarly journals Very Short-Term Wind Power Forecasting: State-of-the-Art

2014 ◽  
Author(s):  
Joana Mendes ◽  
Jean Sumaili ◽  
Ricardo Bessa ◽  
Hrvoje Keko ◽  
Vladimiro Miranda ◽  
...  
Keyword(s):  
Wind Power ◽  
State Of The Art ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Power Forecasting
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