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Long Term Load Demand Forecasting in Bali Province Using Deep Learning Neural Network

机译:基于深度学习神经网络的巴厘岛省长期负荷需求预测

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PT PLN (Persero) is a company of Indonesian electricity state, one of the duty is to deliver the electricity whole of Indonesia. Every ten years, PLN conducts a planning to develop electric infrastructure that referring to a document named RUPTL (Rencana Usaha Penyediaan Tenaga Listrik), one of the parameters that estimated is load consumption in MWh until next ten years. Currently, the existing method forecasting is using Multiple Linear Regression (MLR), but it has deviation against the realization. This paper proposes the Deep Learning Neural Network method which offers better advantage in time series forecasting get smaller deviation in forecasting. This paper using a case by forecasting the load demand in Bali province based on historical data from 2004 - 2018. The evaluation is conducted by comparing the proposed method Deep Learning Neural Network against the existing method Multiple Linear Regression by obtaining the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) value.
机译:PT PLN(Persero)是印度尼西亚电力州的公司,其职责之一是向整个印度尼西亚提供电力。每隔十年,PLN都会根据称为RUPTL的文档(Rencana Usaha Penyediaan Tenaga Listrik)进行一项电力基础设施开发计划,该参数之一是直到下一个十年的兆瓦时负荷消耗。当前,现有的方法预测使用的是多元线性回归(MLR),但与实现存在偏差。本文提出了深度学习神经网络方法,该方法在时间序列预测中具有更好的优势,在预测中具有较小的偏差。本文以2004年至2018年的历史数据为基础,通过预测巴厘岛省的负荷需求来进行案例分析。通过将拟议方法深度学习神经网络与现有方法多重线性回归(通过获得均方根误差( RMSE)和平均绝对百分比误差(MAPE)值。

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