ENHANCEMENT OF SHORT-TERM LOAD FORECASTING BASED ON PARALLEL HYBRID WAVELET NEURAL NETWORK
Short-term load forecasting (STLF) is the prediction of load demands from one hour to one week which crucially is used for operation and planning of the electric power system. Load demands are nonstationary processes and sensitive to the weather conditions. Due to these challenges, STLF requires...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | http://utpedia.utp.edu.my/21856/1/2016%20-%20%20ELECTRICAL%20-%20ENHANCEMENT%20OF%20SHORT-TERM%20LOAD%20FORECASTING%20BASED%20ON%20PARALLEL%20HYBRID%20WAVELET%20NEURAL%20NETWORK-SOVANN%20NARIN-MASTER%20OF%20SCIENCE%20ELECTRICAL%20AND.pdf http://utpedia.utp.edu.my/21856/ |
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| Summary: | Short-term load forecasting (STLF) is the prediction of load demands from one hour to
one week which crucially is used for operation and planning of the electric power
system. Load demands are nonstationary processes and sensitive to the weather
conditions. Due to these challenges, STLF requires a new model that can achieve
accuracy and robustness of load forecasting. This work proposes a hybrid model to
-improve the accuiacy and certainty tor one-day ahead (from 1 hour to 24 hours) load
forecasting. This proposed method is Parallel Hybrid Wavelet Neural Network
(PWNN) which comprises of Wavelet Transform (WT), hybrid particle swarm
optimization and Levenberg-Marquardt algorithm (PSO-LM) and neural network (NN)
based on parallel prediction method. |
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