Results 251 to 260 of about 128,623 (299)

Short term load forecasting by ANN

2009 IEEE Workshop on Hybrid Intelligent Models and Applications, 2009
Short-term load forecasting (STLF) accuracy is very important for the power system. This study explores the application of neural networks to study the design of short-term load forecasting Systems for electricity market of Iran. In this paper, two seasonal artificial neural networks (ANNs) are designed and compared; so that model 2 (hourly load ...
Ali Azadeh, S. Farid Ghadrei, Behnaz Pourvalikhan Nokhandan   +2 more
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Short-Term Load Forecasting by Machine Learning

2020 International Symposium on Community-centric Systems (CcS), 2020
In the global energy transition, Taiwan government has legislated the law to require large-scale power consumers with the obligation to partially use renewable energy. Many companies choose to follow the regulation by purchasing green energy. To purchase the energy effectively, it is necessary to understand its own electricity consumption.
Chung-Chian Hsu, Xiang-Ting Chen, Yu-Sheng Chen, Arthur Chang   +3 more
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Short-term load forecasting based on load profiling

2013 IEEE Power & Energy Society General Meeting, 2013
Load forecasting has gradually becoming a major field of research in electricity industry. Therefore, Load forecasting is extremely important for the electric sector under deregulated environment as it provides a useful support to the power system management. Accurate power load forecasting models are required to the operation and planning of a utility
Ramos, Sérgio, Soares, João, Vale, Zita, Ramos, Sandra   +3 more
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A short-term bus load forecasting system

2010 10th International Conference on Hybrid Intelligent Systems, 2010
This paper proposes a methodology for a short-term bus load forecasting. This approach calculates the short-term bus load demand forecast using few aggregated models. The idea is to cluster the buses in groups with similar daily load profile and for each cluster one bus load forecasting model is adjusted.
Ricardo Menezes Salgado, Takaaki Ohishi, Rosangela Ballini   +2 more
openaire   +1 more source

Standardization of short-term load forecasting models

2012 9th International Conference on the European Energy Market, 2012
There has been a significant production of load forecasting models over the last 5 years. These models present a wide variety of techniques, most of them using novel artificial intelligence approaches. Load forecasting is a complex matter and it is the result of several processes that, depending on the database, may be of more or less importance ...
López García, Miguel, VALERO, SERGIO, Senabre, Carolina, Gabaldon, Antonio, Aparicio, Juan   +4 more
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A Methodology for Short-Term Load Forecasting

IEEE Latin America Transactions, 2017
Demand forecasting is important for electrical analysis development by utilities. It requires low error levels in order to reach reliability in electrical analysis. However, the demand for energy has dissimilar profiles variations depending on the type of day, weather conditions and geographical area.
j. Jiménez, K. Donado, C. G. Quintero
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A hierarchical neural model in short-term load forecasting

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000
This paper proposes a novel neural model to the problem of short-term load forecasting. The neural model is made up of two self-organizing map nets-one on top of the other. It has been successfully applied to domains in which the context information given by former events plays a primary role.
Otávio Augusto Salgado Carpinteiro, Agnaldo J. R. Reis, Alexandre P. Alves da Silva   +2 more
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Short Term Load Forecasting Using XGBoost

2019
For efficient use of smart grid, exact prediction about the in-future coming load is of great importance to the utility. In this proposed scheme initially we converted daily Australian energy market operator load data to weekly data time series. Furthermore, we used eXtreme Gradient Boosting (XGBoost) for extracting features from the data.
Raza Abid Abbasi, Nadeem Javaid, Muhammad Nauman Javid Ghuman, Zahoor Ali Khan, Shujat ur Rehman, Amanullah   +5 more
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Fuzzy interaction regression for short term load forecasting [PDF]

Fuzzy Optimization and Decision Making, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tao Hong 0003, Pu Wang
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Robust short-term load forecasting

International Workshop on Systems, Signal Processing and their Applications, WOSSPA, 2011
Analyzing the stochastic characteristics of electric consumption series in many countries shows the presence of atypical observations or outliers. Outliers are deviant data points that do not follow the model of the majority of observations. They significantly degrade the accuracy of conventional day-ahead estimation and forecasting methods even if ...
Yacine Chakhchoukh, Abdelhak M. Zoubir
openaire   +1 more source