Adaptation of selected aspects of deterministic chaos for long-term forecasts of peak power demand for Poland

2018 ◽  
Vol 1 (12) ◽  
pp. 81-87
Author(s):  
Tomasz POPŁAWSKI
Keyword(s):  
Peak Power ◽  
Deterministic Chaos ◽  
Power Demand

scholarly journals Designing carbon capture power plants to assist in meeting peak power demand

2009 ◽  
Vol 1 (1) ◽  
pp. 1457-1464 ◽  
Author(s):  
M.R. Haines ◽  
J.E. Davison
Keyword(s):  
Carbon Capture ◽  
Peak Power ◽  
Power Plants ◽  
Power Demand

scholarly journals Assessment of peak power demand reduction available via modulation of building ventilation systems.

2020 ◽  
Vol 214 ◽  
pp. 109867
Author(s):  
Matthew Young ◽  
Brennan D. Less ◽  
Spencer M. Dutton ◽  
Iain S. Walker ◽  
Max H. Sherman ◽  
...  
Keyword(s):  
Peak Power ◽  
Power Demand ◽  
Demand Reduction ◽  
Building Ventilation ◽  
Ventilation Systems

scholarly journals Deep Neural Network and Long Short-Term Memory for Electric Power Load Forecasting

2020 ◽  
Vol 10 (18) ◽  
pp. 6489
Author(s):  
Namrye Son ◽  
Seunghak Yang ◽  
Jeongseung Na
Keyword(s):  
Power Consumption ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Power Demand ◽  
Short Term ◽  
Power Load ◽  
Short And Long Term ◽  
Long Short Term Memory ◽  
Electric Power Load

Forecasting domestic and foreign power demand is crucial for planning the operation and expansion of facilities. Power demand patterns are very complex owing to energy market deregulation. Therefore, developing an appropriate power forecasting model for an electrical grid is challenging. In particular, when consumers use power irregularly, the utility cannot accurately predict short- and long-term power consumption. Utilities that experience short- and long-term power demands cannot operate power supplies reliably; in worst-case scenarios, blackouts occur. Therefore, the utility must predict the power demands by analyzing the customers’ power consumption patterns for power supply stabilization. For this, a medium- and long-term power forecasting is proposed. The electricity demand forecast was divided into medium-term and long-term load forecast for customers with different power consumption patterns. Among various deep learning methods, deep neural networks (DNNs) and long short-term memory (LSTM) were employed for the time series prediction. The DNN and LSTM performances were compared to verify the proposed model. The two models were tested, and the results were examined with the accuracies of the six most commonly used evaluation measures in the medium- and long-term electric power load forecasting. The DNN outperformed the LSTM, regardless of the customer’s power pattern.

scholarly journals Stationary energy storage system in a 3 kV DC – the conception comparison

2018 ◽  
Vol 180 ◽  
pp. 02013
Author(s):  
Włodzimierz Jefimowski
Keyword(s):  
Energy Storage ◽  
Peak Power ◽  
Storage System ◽  
Energy Storage System ◽  
Energy Utilization ◽  
Power Demand ◽  
Dc System ◽  
Traction Substation ◽  
Stationary Energy ◽  
Regenerative Energy

The paper presents the research results of a few different conception of stationary energy storage system in a 3 kV DC system. The most attention is focused on the comparison between two topologies of the ESS: energy storage system with supercapacitor and with supercapacitor and LFP battery. The variants are compared in terms of energy saving and peak power demand reduction. The implementation of ESS with SC results the decrease of active energy drawn from traction substation. Meanwhile the implementation of ESS with SC and LFP battery leads to achieving of two aims - decreasing of active energy consumption by maximization of regenerative energy utilization and reduction of 15 - min. peak power demand of traction substation.

scholarly journals Long-Term Amplitude and Period Variations of δ Scuti Stars: A Sign of Chaos?

1993 ◽  
Vol 134 ◽  
pp. 173-180
Author(s):  
M. Breger
Keyword(s):  
Time Scales ◽  
Chaotic Behavior ◽  
Deterministic Chaos ◽  
Observational Evidence ◽  
Mode Interaction ◽  
Mode Switching ◽  
Completely Regular ◽  
Long Term Changes ◽  
Scuti Stars

AbstractOn short time-scales of under a year, the vast majority of δ Scuti stars studied in detail show completely regular multiperiodic pulsation. Nonradial pulsation is characterized by the excitation of a large number of modes with small amplitudes. Reports of short-term irregularity or nonperiodicity in the literature need to be examined carefully, since insufficient observational data can lead to an incorrect impression of irregularity. Some interesting cases of reported irregularities are examined.A few δ Scuti stars, such as 21 Mon, have shown stable variations with sudden mode switching to a new frequency spectrum. This situation might be an indication of deterministic chaos. However, the observational evidence for mode switching is still weak.One the other hand, the case for the existence of long-term amplitude and period changes is becoming quite convincing. Recently found examples of nonradial pulsators with long-term changes are 4 CVn, 44 Tau, τ Peg and HD 2724. (We note that other δ Scuti pulsators such as X Cae and θ2 Tau, have shown no evidence for amplitude variations over the years.) Neither the amplitude nor the period changes are periodic, although irregular cycles with time scales between a few and twenty years can be seen. While the amplitude changes can be very large, the period changes are quite small. This property is common in nonlinear systems which lead to chaotic behavior. There exists observational evidence for relatively sudden period jumps changing the period by about 10−5 and/or slow period changes near dP/dt ≤ 10−9. These period changes are an order of magnitude larger than those expected from stellar evolution.The nonperiodic long-term changes are interpreted in terms of resonances between different nonradial modes. It is shown that a large number of the nonradial acoustic modes can be in resonance with other modes once the mode interaction terms, different radial orders and rotational m-mode splitting are considered. These resonances are illustrated numerically by the use of pulsation model. Observational evidence is presented that these interaction modes exist in the low-frequency domain.

scholarly journals Velocity Loss Thresholds Reliably Control Kinetic and Kinematic Outputs during Free Weight Resistance Training

2020 ◽  
Vol 17 (18) ◽  
pp. 6509
Author(s):  
Madison Pearson ◽  
Amador García-Ramos ◽  
Matthew Morrison ◽  
Carlos Ramirez-Lopez ◽  
Nicholas Dalton-Barron ◽  
...  
Keyword(s):  
Resistance Training ◽  
Coefficient Of Variation ◽  
External Load ◽  
Relative Velocity ◽  
Peak Power ◽  
Crossover Design ◽  
Professional Athletes ◽  
Back Squat ◽  
Free Weight

Exercise velocity and relative velocity loss thresholds (VLTs) are commonly used in velocity-based resistance training. This study aims to quantify the between-day reliability of 10%, 20%, and 30% VLTs on kinetic and kinematic outputs, changes in external load, and repetition characteristics in well-trained athletes. Using a repeated, counter-balanced crossover design, twelve semi-professional athletes completed five sets of the back squat with an external load corresponding to a mean concentric velocity of ~0.70 m·s−1 and a VLT applied. The testing sessions were repeated after four weeks of unstructured training to assess the long-term reliability of each VLT. A coefficient of variation (CV) <10% was used to classify outputs as reliable. Kinetic and kinematic outputs and external load were largely reliable, with only peak power during sets 2–5 within the 10% VLT condition demonstrating a CV >10% (CV: 11.14–14.92%). Alternatively, the repetitions completed within each set showed large variation (CV: 18.92–67.49%). These findings demonstrate that by utilizing VLTs, kinetic and kinematic outputs can be prescribed and replicated across training mesocycles. Thus, for practitioners wishing to reliably control the kinetic and kinematic stimulus that is being applied to their athletes, it is advised that a velocity-based approach is used.

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