TY  - SER
AU  - Pornchai Chaweewat  
AU  - Singh,Jai Govind
AU  - Weerakorn Ongsakul,
AU  - Dhakal,Shobhakar
ED  - PEA-AIT Education Cooperation Project,
ED  - Royal Thai Government Fellowship,
TI  - Electricity price forecasting in smart grid using machine learning
T2  - Dissertation
PY  - 2021///
CY  - Pathum Thani, Thailand
PB  - Asian Institute of Technology
KW  - Electric utilities
KW  - Rates
KW  - Smart power grids
KW  - Electricity
KW  - Marketing
KW  - Machine learning
N1  - A dissertation submitted in partial fulfillment of the requirements for the  degree of Doctor of Engineering in Energy, School of Environment, Resources and Development; Thesis (Ph.D.) - Asian Institute of Technology, 2021
N2  - At present, electricity price forecasts have become a fundamental input to energy utility  and company for decision-making mechanisms. Electricity price forecasting focuses on  predicting the spot and forward prices in the wholesale electricity market. The  electricity price forecasting depends on weather and the intensity of business and  everyday activities. These unique characteristics lead to price dynamics and price  spikes. For these reasons, electricity price forecasting is a big challenge in the energy  market. A variety of methods and ideas have been developed with varying degrees of  success. Computational intelligence models or call machine learning techniques gain  popularity in recent years because their major strength is the ability to handle  complexity and non-linearity, like electricity price volatility. Therefore, this study will  focus on modeling electricity price forecasting based on machine learning techniques with real data simulation.  In this work, to examine electricity price forecasting, the proposed electricity  forecasting models were formulated based on conventional and modern machine  learning techniques. The novel residual neural network in electricity price forecasting  was first introduced in this study. The simulation results were evaluated using the  coverage width-base criterion, which showed that the proposed model could improve  forecasting results accurately and reliable. The average forecasting error was reduced  by about 13% error with comparing to multilayer perceptron models. Moreover, this study tried to reduce forecasting error by improving electricity demand  and renewable energy resource forecasting. The proposed forecasting model cooperated  with demand and renewable energy resource forecasts. The results were reduced below  1%, while the benchmark models are around 5% error.
UR  - http://203.159.5.9/ait-thesis/detail.php?q=B16667
ER  -