scholarly journals A New Approach to Peak Load Forecasting based on EMD and ANFIS

2013 ◽  
Vol 6 (12) ◽  
pp. 1-7 ◽  
Author(s):  
Zuhaimy Ismail
Keyword(s):  
Peak Load ◽  
Load Forecasting ◽  
New Approach

Peak Load Forecasting Using Long-Short Term Memory : Case Study of Jawa-Madura-Bali System

2021 ◽  
Author(s):  
Anindita Satria Surya ◽  
Musa Partahi Marbun ◽  
K.G.H. Mangunkusumo ◽  
Muhammad Ridwan
Keyword(s):  
Short Term Memory ◽  
Peak Load ◽  
Load Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

ANN technique based Mid Term Load Forecasting as a case study for Peak Load Reduction

2018 ◽  
Author(s):  
Ayandeep Ganguly ◽  
Kuheli Goswami ◽  
Arindam Kumar Sil
Keyword(s):  
Peak Load ◽  
Load Forecasting ◽  
Load Reduction

scholarly journals Daily Peak Load Forecasting by Artificial Neural Network using Differential Evolutionary Particle Swarm Optimization Considering Outliers

2019 ◽  
Vol 52 (4) ◽  
pp. 389-394 ◽  
Author(s):  
Daiji Sakurai ◽  
Yoshikazu Fukuyama ◽  
Tatsuya Iizaka ◽  
Tetsuro Matsui
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Peak Load ◽  
Load Forecasting ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Artificial Neural

scholarly journals Practical Operation Strategies for Energy Storage System under Uncertainty

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1098 ◽  
Author(s):  
Minsoo Kim ◽  
Kangsan Kim ◽  
Hyungeun Choi ◽  
Seonjeong Lee ◽  
Hongseok Kim
Keyword(s):  
Real Time ◽  
Peak Power ◽  
Storage System ◽  
Peak Load ◽  
Load Forecasting ◽  
Energy Storage System ◽  
Real Time Control ◽  
Time Control ◽  
Forecasting Error ◽  
Marginal Power

Recent advances in battery technologies have reduced the financial burden of using the energy storage system (ESS) for customers. Peak cut, one of the benefits of using ESS, can be achieved through proper charging/discharging scheduling of ESS. However, peak cut is sensitive to load-forecasting error, and even a small forecasting error may result in the failure of peak cut. In this paper, we propose a two-phase approach of day-ahead optimization and real-time control for minimizing the total cost that comes from time-of-use (TOU), peak load, and battery degradation. In day-ahead optimization, we propose to use an internalized pricing to manage peak load in addition to the cost from TOU. The proposed method can be implemented by using dynamic programming, which also has an advantage of accommodating the state-dependent battery degradation cost. Then in real-time control, we propose a concept of marginal power to alleviate the performance loss incurred from load-forecasting error and mimic the offline optimal battery scheduling by learning from load-forecasting error. By exploiting the marginal power, real-time ESS charging/discharging power gets close to the offline optimal battery scheduling. Case studies show that under load-forecasting uncertainty, the peak power using the proposed method is only 22.4% higher than the offline optimal peak power, while the day-ahead optimization has 76.8% higher peak power than the offline optimal power. In terms of profit, the proposed method achieves 77.0% of the offline optimal profit while the day-ahead method only earns 19.6% of the offline optimal profit, which shows the substantial improvement of the proposed method.

Peak load forecasting of Electricity Generating Authority of Thailand by Gaussian Process

2014 ◽  
Author(s):  
Tuchsanai Ploysuwan ◽  
Pramukpong Atsawathawichok ◽  
Prasit Teekaput
Keyword(s):  
Gaussian Process ◽  
Peak Load ◽  
Load Forecasting

scholarly journals GCC electrical long-term peak load forecasting modeling using ANFIS and MLR methods

2019 ◽  
Vol 26 (1) ◽  
pp. 269-282 ◽  
Author(s):  
Mohamed Y. AL-Hamad ◽  
Isa S. Qamber
Keyword(s):  
Peak Load ◽  
Load Forecasting
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