Effect of Neural Network structure for daily electricity load forecasting

机译：神经网络结构对日常电力负荷预测的影响

获取原文

页面导航

摘要
著录项
相似文献
相关主题

摘要

Accurate electricity demand forecasts are critical for daily operations planning. They influence many decisions, including commits to produce electricity for a given period. This paper presents a short term electricity demand forecasting system using the Artificial Neural Networks (ANNs). The model is trained and tested on 30-minutes historical load data with temperature from the Electricity Generating Authority of Thailand (EGAT) from January 1, 2012 to December 31, 2013. The ANNs use historical load data with temperature to forecast daily electricity demand in Thailand. Holidays, bridging holidays, and outliers of the raw data are detected and replaced. Historical load (previous day, previous week), forecasted day total load, forecasted day temperature, previous day temperature, calendar days (Day of week and Month), and whether the forecasted day is a holiday or not are used as input parameters. The forecasting performances are compared with Regression model. Best performance has shown with ANN.

机译：准确的电力需求预测对于日常运营规划至关重要。它们影响了许多决定，包括为特定时期产生电力。本文介绍了使用人工神经网络（ANNS）的短期电力需求预测系统。该模型在2012年1月1日至2013年1月1日至2013年12月31日的泰国（EGAT）的电力发电权威于30分钟的历史载荷数据进行培训和测试。ANNS使用温度历史负荷数据来预测每日电力需求泰国。检测和更换原始数据的假期，桥接假期和异常值。历史负荷（前一天，上周），预测日总负荷，预测日温度，前一天温度，日历日（一周中的一天），以及预测的一天是假期是否用作输入参数。与回归模型进行比较预测性能。最佳性能显示了ANN。

著录项

来源
《Moratuwa Engineering Research Conference》|2017年|xxii 496 p.|共6页
会议地点
作者
M H M R Shyamali Dilhani; Chawalit Jeenanunta;
展开▼
作者单位

展开▼
会议组织
原文格式 PDF
正文语种
中图分类输配电工程、电力网及电力系统;
关键词
Load modeling; Artificial neural networks; Forecasting; Load forecasting; Neurons; Training; Temperature distribution;

机译：负荷建模;人工神经网络;预测;负荷预测;神经元;训练;温度分布;

相似文献

外文文献
中文文献
专利

1. Forecast of Daily Maximum Electric Load by Neural Networks using the Standard Electric Load [J] . Yasuo Sugai, Hiroshi Horibe, Taro Kawase 電気学会論文誌. B . 1997,第6期

机译：使用标准电力负荷的神经网络预测每日最大电力负荷
2. Forecasting the Daily Electricity Consumption in the Moscow Region Using Artificial Neural Networks [J] . V. V. Ivanov, A. V. Kryanev, E. S. Osetrov Physics of particles and nuclei letters . 2017,第4期

机译：使用人工神经网络预测莫斯科地区的日常电力消耗
3. Joint bagged-boosted artificial neural networks: Using ensemble machine learning to improve short-term electricity load forecasting [J] . Khwaja A. S., Anpalagan A., Naeem M., Electric power systems research . 2020,第Feba期

机译：联合袋装增强人工神经网络：使用集成机器学习来改善短期电力负荷预测
4. Effect of Neural Network structure for daily electricity load forecasting [C] . M H M R Shyamali Dilhani, Chawalit Jeenanunta Moratuwa Engineering Research Conference . 2017

机译：神经网络结构对每日电力负荷预测的影响
5. Comparison of regression and ARIMA models with neural network models to forecast the daily streamflow of White Clay Creek. [D] . Liu, Greg Qi. 2011

机译：将回归模型和ARIMA模型与神经网络模型进行比较，以预测White Clay Creek的日流量。
6. Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market [O] . Ömer Özgür Bozkurt, Göksel Biricik, Ziya Cihan Tayşi -1

机译：土耳其电力市场中基于人工神经网络和SARIMA的电力负荷预测模型
7. Use of Artificial Neural Networks for Short-Term Electricity Load Forecasting of Kenya National Grid Power System [O] . Christopher A.Moturi, Francis K. Kioko 2013

机译：使用人工神经网络用于肯尼亚国家电网电力系统的短期电力负荷预测

Effect of Neural Network structure for daily electricity load forecasting

摘要

著录项

相似文献

相关主题

期刊订阅