Permanent Solutions for Temporary Loads an Alternate Approach to Electric System Design for Short-Term Loading

2016 ◽  
Author(s):  
Aaron Potts ◽  
Eli Immasche ◽  
Christopher Brooks ◽  
Lloyd Sisson ◽  
Stephan Sundet
Keyword(s):  
System Design ◽  
Short Term ◽  
Electric System

A Deep Learning Based Approach to Predict Sequential Design Decisions

2019 ◽  
Author(s):  
Molla Hafizur Rahman ◽  
Charles Xie ◽  
Zhenghui Sha
Keyword(s):  
Markov Chain ◽  
System Design ◽  
Design Process ◽  
Design Space ◽  
Markov Chain Model ◽  
Sequential Design ◽  
Chain Model ◽  
Short Term ◽  
Design Decisions ◽  
Term Memory

Abstract During a design process, designers iteratively go back and forth between different design stages to explore the design space and search for the best design solution that satisfies all design constraints. For complex design problems, human has shown surprising capability in effectively reducing the dimensionality of design space and quickly converging it to a reasonable range for algorithms to step in and continue the search process. Therefore, modeling how human designers make decisions in such a sequential design process can help discover beneficial design patterns, strategies, and heuristics, which are important to the development of new algorithms embedded with human intelligence to augment computational design. In this paper, we develop a deep learning based approach to model and predict designers’ sequential decisions in a system design context. The core of this approach is an integration of the function-behavior-structure model for design process characterization and the long short term memory unit model for deep leaning. This approach is demonstrated in a solar energy system design case study, and its prediction accuracy is evaluated benchmarked on several commonly used models for sequential design decisions, such as Markov Chain model, Hidden Markov Chain model, and random sequence generation model. The results indicate that the proposed approach outperforms the other traditional models. This implies that during a system design task, designers are very likely to reply on both short-term and long-term memory of past design decisions in guiding their decision making in future design process. Our approach is general to be applied in many other design contexts as long as the sequential design action data is available.

scholarly journals Analysis of Different Neural Networks and a New Architecture for Short-Term Load Forecasting

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1433 ◽  
Author(s):  
Lintao Yang ◽  
Honggeng Yang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Scale ◽  
Load Forecasting ◽  
Short Term ◽  
Electric System ◽  
Power Company ◽  
Network Methods ◽  
Short Term Load Forecasting ◽  
Electric Power Company

Short-term load forecasting (STLF) has been widely studied because it plays a very important role in improving the economy and security of electric system operations. Many types of neural networks have been successfully used for STLF. In most of these methods, common neural networks were used, but without a systematic comparative analysis. In this paper, we first compare the most frequently used neural networks’ performance on the load dataset from the State Grid Sichuan Electric Power Company (China). Then, considering the current neural networks’ disadvantages, we propose a new architecture called a gate-recurrent neural network (RNN) based on an RNN for STLF. By evaluating all the methods on our dataset, the results demonstrate that the performance of different neural network methods are related to the data time scale, and our proposed method is more accurate on a much shorter time scale, particularly when the time scale is smaller than 20 min.

Short-Term Contracts, Long-Term Actions, and Information System Design

2004 ◽  
Vol 19 (2) ◽  
pp. 117-140
Author(s):  
Ramji Balakrishnan ◽  
K. Sivaramakrishnan ◽  
K. Surysekar
Keyword(s):  
Information System ◽  
System Design ◽  
Short Term ◽  
Information System Design

scholarly journals Use of Available Daylight to Improve Short-Term Load Forecasting Accuracy

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 95
Author(s):  
Miguel López ◽  
Sergio Valero ◽  
Carlos Sans ◽  
Carolina Senabre
Keyword(s):  
Load Forecasting ◽  
Linearization Method ◽  
Ar Model ◽  
Forecasting Model ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Electric System ◽  
Forecasting Error ◽  
Short Term Load Forecasting ◽  
System Operator

This paper introduces a new methodology to include daylight information in short-term load forecasting (STLF) models. The relation between daylight and power consumption is obvious due to the use of electricity in lighting in general. Nevertheless, very few STLF systems include this variable as an input. In addition, an analysis of one of the current STLF models at the Spanish Transmission System Operator (TSO), shows two humps in its error profile, occurring at sunrise and sunset times. The new methodology includes properly treated daylight information in STLF models in order to reduce the forecasting error during sunrise and sunset, especially when daylight savings time (DST) one-hour time shifts occur. This paper describes the raw information and the linearization method needed. The forecasting model used as the benchmark is currently used at the TSO’s headquarters and it uses both autoregressive (AR) and neural network (NN) components. The method has been designed with data from the Spanish electric system from 2011 to 2017 and tested over 2018 data. The results include a justification to use the proposed linearization over other techniques as well as a thorough analysis of the forecast results yielding an error reduction in sunset hours from 1.56% to 1.38% for the AR model and from 1.37% to 1.30% for the combined forecast. In addition, during the weeks in which DST shifts are implemented, sunset error drops from 2.53% to 2.09%.

Sign in / Sign up

Export Citation Format

Share Document