LOAD SCHEDULING FOR SMART HOME USING DAY-AHEAD PREDICTION FROM ARTIFICIAL NEURAL NETWORK (ANN)
نویسندگان
چکیده
منابع مشابه
One Day Ahead Load Forecasting Using Recurrent Neural Network
This paper presents short term load forecasting (STLF) in Java Island using recurrent neural network (RNN). The simple one of RNN is Elman, it has one hidden layer and suitable used in time series prediction. It can learn an input-output mapping which is nonlinear. The Elman RNN was proposed for one day a head forecasting, with interval time 30 minutes. Training model divided into weekday, week...
متن کاملA Modified Feature Selection and Artificial Neural Network-Based Day-Ahead Load Forecasting Model for a Smart Grid
In the operation of a smart grid (SG), day-ahead load forecasting (DLF) is an important task. The SG can enhance the management of its conventional and renewable resources with a more accurate DLF model. However, DLF model development is highly challenging due to the non-linear characteristics of load time series in SGs. In the literature, DLF models do exist; however, these models trade off be...
متن کاملPrediction of skin penetration using artificial neural network (ANN) modeling.
Artificial neural network (ANN) analysis was used to predict the skin permeability of selected xenobiotics. Permeability coefficients (log k(p)) were obtained from various literature sources. A previously reported equation, which was shown to be useful in the prediction of skin permeability, uses the partial charges of the penetrants, their molecular weight, and their calculated octanol water p...
متن کاملShort-term Prediction of Tehran Stock Exchange Price Index (TEPIX): Using Artificial Neural Network (ANN)
The main objective of this study is to find out whether an Artificial Neural Network (ANN) will be useful to predict stock market price, which is highly non-linear and uncertain. Specifically, this study will focus on forecasting TSE Price Index (TEPIX) as the most significant index of Iran Stock Market. Many data have been used as inputs to the network. These data are observations of 2000 day...
متن کاملPrediction of Time of Capillary Rise in Porous Media Using Artificial Neural Network (ANN)
An Artificial Neural Network (ANN) was used to analyse the capillary rise in porous media. Wetting experiments were performed with fifteen liquids and fifteen different powders. The liquids covered a wide range of surface tension ( 15.45-71.99 mJ/m2 ) and viscosity (0.25-21 mPa.s). The powders also provided an acceptable range of particle size (0.012-45 μm) and surface free...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Advanced Trends in Computer Science and Engineering
سال: 2020
ISSN: 2278-3091
DOI: 10.30534/ijatcse/2020/9291.42020