Hybrid Model For The Next Hourly Electricity Load Demand Forecasting Based on Clustering and Weather Data

2020 ◽  
Author(s):  
Unit Three Kartini ◽  
Deddy Putra Ardyansyah ◽  
Eppy Yundra
Keyword(s):  
Hybrid Model ◽  
Demand Forecasting ◽  
Weather Data ◽  
Load Demand ◽  
Electricity Load

Random vector functional link network for short-term electricity load demand forecasting

2016 ◽  
Vol 367-368 ◽  
pp. 1078-1093 ◽  
Author(s):  
Ye Ren ◽  
P.N. Suganthan ◽  
N. Srikanth ◽  
Gehan Amaratunga
Keyword(s):  
Random Vector ◽  
Demand Forecasting ◽  
Short Term ◽  
Functional Link ◽  
Load Demand ◽  
Electricity Load

scholarly journals Forecasting electricity load demand using hybrid exponential smoothing-artificial neural network model

2016 ◽  
Vol 2 (3) ◽  
pp. 131 ◽  
Author(s):  
Winita Sulandari ◽  
Subanar Subanar ◽  
Suhartono Suhartono ◽  
Herni Utami
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Network Model ◽  
Neural Network Model ◽  
Exponential Smoothing ◽  
Series Data ◽  
Demand Forecast ◽  
Forecast Performance ◽  
Load Demand ◽  
Electricity Load

Short-term electricity load demand forecast is a vital requirements for power systems. This research considers the combination of exponential smoothing for double seasonal patterns and neural network model. The linear version of Holt-Winter method is extended to accommodate a second seasonal component. In this work, the Fourier with time varying coefficient is presented as a means of seasonal extraction. The methodological contribution of this paper is to demonstrate how these methods can be adapted to model the time series data with multiple seasonal pattern, correlated non stationary error and nonlinearity components together. The proposed hybrid model is started by implementing exponential smoothing state space model to obtain the level, trend, seasonal and irregular components and then use them as inputs of neural network. Forecasts of future values are then can be obtained by using the hybrid model. The forecast performance was characterized by root mean square error and mean absolute percentage error. The proposed hybrid model is applied to two real load series that are energy consumption in Bawen substation and in Java-Bali area. Comparing with other existing models, results show that the proposed hybrid model generate the most accurate forecast

Electricity Load/demand Forecasting in Sri Lanka using Deep Learning Techniques

2021 ◽  
Author(s):  
A.A.K.U Abeysingha ◽  
Sritharan A.S ◽  
R Valluvan ◽  
K Ahilan ◽  
D.H.G.A.E Jayasinghe
Keyword(s):  
Deep Learning ◽  
Sri Lanka ◽  
Demand Forecasting ◽  
Load Demand ◽  
Learning Techniques ◽  
Electricity Load

Improving the Model for Energy Consumption Load Demand Forecasting

2012 ◽  
Vol 132 (3) ◽  
pp. 235-243
Author(s):  
Pituk Bunnoon ◽  
Kusumal Chalermyanont ◽  
Chusak Limsakul
Keyword(s):  
Energy Consumption ◽  
Demand Forecasting ◽  
Load Demand
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