Deep Neural Network Regression for Short-Term Load Forecasting of Natural Gas
نویسندگان
چکیده
This paper proposes a short-term load forecasting method for natural gas using deep learning. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields like image recognition and speech processing. This paper explores many aspects of using deep neural networks for time series forecasting. It is determined that the proposed network outperforms traditional artificial neural networks and linear regression based forecasters.
منابع مشابه
Short-term and Medium-term Gas Demand Load Forecasting by Neural Networks
The ability of Artificial Neural Network (ANN) for estimating the natural gas demand load for the next day and month of the populated cities has shown to be a real concern. As the most applicable network, the ANN with multi-layer back propagation perceptrons is used to approximate functions. Throughout the current work, the daily effective temperature is determined, and then the weather data w...
متن کاملShort term electric load prediction based on deep neural network and wavelet transform and input selection
Electricity demand forecasting is one of the most important factors in the planning, design, and operation of competitive electrical systems. However, most of the load forecasting methods are not accurate. Therefore, in order to increase the accuracy of the short-term electrical load forecast, this paper proposes a hybrid method for predicting electric load based on a deep neural network with a...
متن کاملShort Term Load Forecasting by Using ESN Neural Network Hamedan Province Case Study
Abstract Forecasting electrical energy demand and consumption is one of the important decision-making tools in distributing companies for making contracts scheduling and purchasing electrical energy. This paper studies load consumption modeling in Hamedan city province distribution network by applying ESN neural network. Weather forecasting data such as minimum day temperature, average day temp...
متن کاملEfficient Short-Term Electricity Load Forecasting Using Recurrent Neural Networks
Short term load forecasting (STLF) plays an important role in the economic and reliable operation ofpower systems. Electric load demand has a complex profile with many multivariable and nonlineardependencies. In this study, recurrent neural network (RNN) architecture is presented for STLF. Theproposed model is capable of forecasting next 24-hour load profile. The main feature in this networkis ...
متن کاملForecasting Natural Gas Demand Using Meteorological Data: Neural Network Method
The need for prediction and patterns of gas consumption especially in the cold seasons is essential for consumption management and policy planning decision making. In residential and commercial uses which account for the bulk of gas consumption in the country the effects of meteorological variables have the highest impact on consumption. In the present research four variables include daily ave...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2017