Mars Phoenix Scout Parachute Canopy Structural Test Peak Load Prediction Technique

2007 ◽  
Author(s):  
Yaro Taeger ◽  
Allen Witkowski ◽  
Mike Kandis
Keyword(s):  
Peak Load ◽  
Load Prediction ◽  
Structural Test ◽  
Prediction Technique ◽  
Parachute Canopy

Peak load prediction of multi-pin joints FRP laminates by limit analysis

2013 ◽  
Vol 96 ◽  
pp. 763-772 ◽  
Author(s):  
A.A. Pisano ◽  
P. Fuschi ◽  
D. De Domenico
Keyword(s):  
Limit Analysis ◽  
Peak Load ◽  
Load Prediction

Daily peak-based short-term demand prediction using backpropagation combined to chi-squared distribution

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Nisrine Kebir ◽  
Abdessamad Lamallam ◽  
Abdelqoddous Moussa
Keyword(s):  
Power Systems ◽  
Power Plants ◽  
Peak Load ◽  
Forecast Accuracy ◽  
Load Prediction ◽  
Demand Forecast ◽  
Short Term ◽  
Demand Prediction ◽  
Chi Squared ◽  
The Impact

AbstractAn efficient and economic scheduling of power plants relies on an accurate demand forecast especially for the short-term due to its tight relation to power markets and trading operations in interconnected power systems. A slight deviation of load prediction from real demand could engender the start-up of a conventional power station which could be either time-consuming or requiring expensive combustible, a deviation that could interfere as well with renewables intermittency and demand response strategies. Hence, load forecasting still a challenging subject because of the various transformations that the energy sector undergoes and that directly impact the demand profile shape. Therefore, conceiving dynamic load demand forecast approaches will permit utilities save money in different vertical structures and regulation schemes. In this paper, we propose a novel approach for short-term demand prediction valid for normal and special days to address the impact of climate changes along with events occurrence on forecast accuracy. This approach is based on the prediction of hourly loads, established on the daily peak load prediction using backpropagation combined to chi-squared method for weighting historical data to enhance the training process. Obtained results from extensive testing on the Moroccan’s power system confirm the strength of the developed approach, that improved the forecast accuracy by a range of 1.1–4% compared to the existing methods.

scholarly journals Peak Load Prediction Using Fuzzy Logic For The 150 kV Sulselrabar System

2018 ◽  
Vol 3 (1) ◽  
pp. 49
Author(s):  
Muhammad Ruswandi Djalal ◽  
Andareas Pangkung ◽  
Sonong Sonong ◽  
Apollo Apollo
Keyword(s):  
Fuzzy Logic ◽  
Data Processing ◽  
Peak Load ◽  
Load Prediction ◽  
Logic Control ◽  
Forecasting Error ◽  
Control Load ◽  
Load Characteristics ◽  
Special Approach ◽  
Comparison Data

Prediction of electrical load on 150 kV Sulselrabar electrical system, analyzed using approach at night peak load using Fuzzy Logic based intelligent method. The peak load characteristics are certainly different from the load in normal time, therefore a special approach is needed to predict the peak night load. As input data will be used data of night peak load in 2010 until 2015, on the same day and date, each 4 days before day-H or day date which will be predicted load. For the data processing stage is divided into several stages, namely pre-processing, processing, and post-processing. The load data processing follows several procedures, ie computing WDmax, LDmax, TLDmax and VLDmax each year. Data processing is processed using excel software and then using Matlab software to run Fuzzy Logic. From the analysis results obtained, Error Prediction The peak evening load is very small that is equal to -0.070033687%. As comparison data used actual day-H data is April 2016. The graph of analysis result also shown in this paper.Keywords– Fuzzy Logic Control, Load Forecasting, Error, VLDmax

scholarly journals Electrical Load Forecasting: A methodological overview

2020 ◽  
Vol 9 (3) ◽  
pp. 842
Author(s):  
Medhat Rostum ◽  
Amr Zamel ◽  
Hassan Moustafa ◽  
Ibrahim Ziedan
Keyword(s):  
Peak Load ◽  
Load Forecasting ◽  
Electric Load ◽  
Load Prediction ◽  
Future Directions ◽  
Forecasting Error ◽  
Load Demand ◽  
Electrical Load Forecasting ◽  
Security And Reliability ◽  
Electric Load Forecasting

Electric load forecasting process plays an extensive role in forecasting future electric load demand and peak load by understanding the previous data. Several researchers proved that, the presence of load forecasting error leads to an increase in operating costs. Thus Accurate electric load forecast is needed for power system security and reliability. It also improves energy efficiency, revenues for the electrical companies and reliable operation of a power system.In recent times, there are significant proliferations in the implementation of forecasting techniques. This survey aids readers to summarize and compare the latest predominant researches on electric load forecasting. Besides, it presents the most relevant studies on load forecasting over the last decade and discusses the different methods that are used in load prediction as well as the future directions in this field.

scholarly journals Development of a Two-Stage ESS-Scheduling Model for Cost Minimization Using Machine Learning-Based Load Prediction Techniques

Processes ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 370 ◽  
Author(s):  
Minsu Park ◽  
Jaehwi Kim ◽  
Dongjun Won ◽  
Jaehee Kim
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Operating Cost ◽  
Peak Load ◽  
Energy System ◽  
Machine Learning Techniques ◽  
Load Prediction ◽  
Two Stage ◽  
Scheduling Model ◽  
Remaining Capacity

Effective use of energy storage systems (ESS) is important to reduce unnecessary power consumption. In this paper, a day-ahead two-stage ESS-scheduling model based on the use of a machine learning technique for load prediction has been proposed for minimizing the operating cost of the energy system. The proposed algorithm consists of two stages of ESS. In the first stage, ESS is used to minimize demand charges by reducing the peak load. Then, the remaining capacity is used to reduce energy charges through arbitrage trading, thereby minimizing the total operating cost. To achieve this purpose, accurate load prediction is required. Machine learning techniques are promising methods owing to the ability to improve forecasting performance. Among them, ensemble learning is a well-known machine learning method which helps to reduce variance and prevent overfitting of a model. To predict loads, we employed bootstrap aggregating (bagging) or random forest technique-based decision trees after Holt–Winters smoothing for trends. Our combined method can increase the prediction accuracy. In the simulation conducted, three combined prediction models were evaluated. The prediction task was performed using the R programming language. The effectiveness of the proposed algorithm was verified by using Python’s PuLP library.

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