{"title":"基于线性回归和人工神经网络的印度电力系统短期负荷预测","authors":"Harsh Patel, M. Pandya, M. Aware","doi":"10.1109/NUICONE.2015.7449617","DOIUrl":null,"url":null,"abstract":"The hour ahead load forecasting is used for the reliable and proactive operation of the power system. The hour ahead load forecasting is a one type of Short Term Load Forecasting (STLF). The mostly STLF is used for the spinning reserve capacity, unit commitment and maintenance planning in the power system. In this paper the Linear Regression (LR) and the Artificial Neural Network (ANN) are used to study the STLF. In the ANN feed forward network is used for the hourly load forecasting. One fast training algorithm the Levenberg-Marquardt Back Propagation (LMBP) is used to train the neural network. The neuron model is trained using the historical load data of Indian distribution system. The sensitivity of the weather data for the STLF is verified. Both the techniques the LR and the ANN are compared according to the Mean Absolute Error (MAE) and the Mean Absolute Percentage Error (MAPE). The accuracy of the ANN technique for the STLF with the weather data is proved for the residential and the industrial feeder.","PeriodicalId":131332,"journal":{"name":"2015 5th Nirma University International Conference on Engineering (NUiCONE)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Short term load forecasting of Indian system using linear regression and artificial neural network\",\"authors\":\"Harsh Patel, M. Pandya, M. Aware\",\"doi\":\"10.1109/NUICONE.2015.7449617\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The hour ahead load forecasting is used for the reliable and proactive operation of the power system. The hour ahead load forecasting is a one type of Short Term Load Forecasting (STLF). The mostly STLF is used for the spinning reserve capacity, unit commitment and maintenance planning in the power system. In this paper the Linear Regression (LR) and the Artificial Neural Network (ANN) are used to study the STLF. In the ANN feed forward network is used for the hourly load forecasting. One fast training algorithm the Levenberg-Marquardt Back Propagation (LMBP) is used to train the neural network. The neuron model is trained using the historical load data of Indian distribution system. The sensitivity of the weather data for the STLF is verified. Both the techniques the LR and the ANN are compared according to the Mean Absolute Error (MAE) and the Mean Absolute Percentage Error (MAPE). The accuracy of the ANN technique for the STLF with the weather data is proved for the residential and the industrial feeder.\",\"PeriodicalId\":131332,\"journal\":{\"name\":\"2015 5th Nirma University International Conference on Engineering (NUiCONE)\",\"volume\":\"96 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 5th Nirma University International Conference on Engineering (NUiCONE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NUICONE.2015.7449617\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 5th Nirma University International Conference on Engineering (NUiCONE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NUICONE.2015.7449617","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
摘要
小时前负荷预测是为了保证电力系统的可靠、主动运行。小时前负荷预测是短期负荷预测的一种。STLF主要用于电力系统的旋转备用容量、机组承诺和维护计划。本文采用线性回归(LR)和人工神经网络(ANN)对STLF进行了研究。在人工神经网络中,前馈网络用于小时负荷预测。采用一种快速训练算法Levenberg-Marquardt Back Propagation (LMBP)对神经网络进行训练。利用印度配电系统的历史负荷数据对神经元模型进行训练。验证了STLF天气资料的敏感性。根据平均绝对误差(MAE)和平均绝对百分比误差(MAPE)对人工神经网络和LR技术进行了比较。在住宅和工业馈电系统中,对天气数据进行人工神经网络技术的精度进行了验证。
Short term load forecasting of Indian system using linear regression and artificial neural network
The hour ahead load forecasting is used for the reliable and proactive operation of the power system. The hour ahead load forecasting is a one type of Short Term Load Forecasting (STLF). The mostly STLF is used for the spinning reserve capacity, unit commitment and maintenance planning in the power system. In this paper the Linear Regression (LR) and the Artificial Neural Network (ANN) are used to study the STLF. In the ANN feed forward network is used for the hourly load forecasting. One fast training algorithm the Levenberg-Marquardt Back Propagation (LMBP) is used to train the neural network. The neuron model is trained using the historical load data of Indian distribution system. The sensitivity of the weather data for the STLF is verified. Both the techniques the LR and the ANN are compared according to the Mean Absolute Error (MAE) and the Mean Absolute Percentage Error (MAPE). The accuracy of the ANN technique for the STLF with the weather data is proved for the residential and the industrial feeder.