基于特征选择的人工神经网络电价预测有效性评价

2017 International Conference on Emerging Trends in Computing and Communication Technologies (ICETCCT) Pub Date : 2017-11-01 DOI:10.1109/ICETCCT.2017.8280298

Neeraj Kumar, M. M. Tripathi

{"title":"基于特征选择的人工神经网络电价预测有效性评价","authors":"Neeraj Kumar, M. M. Tripathi","doi":"10.1109/ICETCCT.2017.8280298","DOIUrl":null,"url":null,"abstract":"Electricity price is one of the most concurrent aspects of power system planning. An accurate method of forecasting is required for several economic and operational advantages. In this paper effectiveness of artificial neural network (ANN) is evaluated by selecting the most influential input variable based forecasting of price in Australian electricity market using the price and total demand data of Queensland (QLD) region from January 2016 to June 2017. Using these data, monthly and weekly forecasting of electricity price is carried out. The mean absolute percentage error (MAPE) and root mean square error (RMSE) is determined by evaluating the effectiveness of proposed method. The best result shows the monthly MAPE as 1.94% and weekly MAPE as 1.06% considering only total demand as input to the ANN.","PeriodicalId":436902,"journal":{"name":"2017 International Conference on Emerging Trends in Computing and Communication Technologies (ICETCCT)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Evaluation of effectiveness of ANN for feature selection based electricity price forecasting\",\"authors\":\"Neeraj Kumar, M. M. Tripathi\",\"doi\":\"10.1109/ICETCCT.2017.8280298\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Electricity price is one of the most concurrent aspects of power system planning. An accurate method of forecasting is required for several economic and operational advantages. In this paper effectiveness of artificial neural network (ANN) is evaluated by selecting the most influential input variable based forecasting of price in Australian electricity market using the price and total demand data of Queensland (QLD) region from January 2016 to June 2017. Using these data, monthly and weekly forecasting of electricity price is carried out. The mean absolute percentage error (MAPE) and root mean square error (RMSE) is determined by evaluating the effectiveness of proposed method. The best result shows the monthly MAPE as 1.94% and weekly MAPE as 1.06% considering only total demand as input to the ANN.\",\"PeriodicalId\":436902,\"journal\":{\"name\":\"2017 International Conference on Emerging Trends in Computing and Communication Technologies (ICETCCT)\",\"volume\":\"47 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Conference on Emerging Trends in Computing and Communication Technologies (ICETCCT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICETCCT.2017.8280298\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Emerging Trends in Computing and Communication Technologies (ICETCCT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICETCCT.2017.8280298","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 2

摘要

电价是电力系统规划中最重要的环节之一。需要一种准确的预测方法，以获得若干经济和操作优势。本文利用2016年1月至2017年6月昆士兰州地区的电价和总需求数据，选择最具影响力的输入变量，对澳大利亚电力市场的电价进行预测，评估人工神经网络(ANN)的有效性。利用这些数据进行了月度和每周电价预测。通过评价方法的有效性，确定了平均绝对百分比误差(MAPE)和均方根误差(RMSE)。仅考虑总需求作为人工神经网络输入，最佳结果显示月度MAPE为1.94%，周MAPE为1.06%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Evaluation of effectiveness of ANN for feature selection based electricity price forecasting

Electricity price is one of the most concurrent aspects of power system planning. An accurate method of forecasting is required for several economic and operational advantages. In this paper effectiveness of artificial neural network (ANN) is evaluated by selecting the most influential input variable based forecasting of price in Australian electricity market using the price and total demand data of Queensland (QLD) region from January 2016 to June 2017. Using these data, monthly and weekly forecasting of electricity price is carried out. The mean absolute percentage error (MAPE) and root mean square error (RMSE) is determined by evaluating the effectiveness of proposed method. The best result shows the monthly MAPE as 1.94% and weekly MAPE as 1.06% considering only total demand as input to the ANN.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2017 International Conference on Emerging Trends in Computing and Communication Technologies (ICETCCT)

自引率

0.00%

发文量