SHORT TERM LOAD FORECASTING USING HYBRID NEURO- WAVELET MODEL (original) (raw)
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Short term load forecasting is a key issue in operating of electricity system. Many operating decisions are based on load forecast such as: dispatch scheduling of generating production, reliability and security analysis and maintenance plan for generators. Artificial neural networks (ANNs) have frequently been proposed for short-term load forecasting (STLF), because of their capabilities for nonlinear modelling of large multivariate datasets. This paper present a short-term load forecasting strategies based on ANN with multi-layer perceptron (MLP) structure and Levenberg–Marquardt (LM) learning algorithm. Also, impact of wavelet transform (WT) in prediction accuracy is studied. Proposed method is implemented in real case study of Zanjan power system.
Short term load forecasting is critically important in modern electricity networks since it helps provide supportive information for reliable power system operation in competitive electricity market environment. In this paper, the wavelet analysis based neural network model is employed to forecast the electricity demand in short-term period. The wavelet analysis helps to decompose the electricity demand data into different frequency bands. The Fourier transform is then employed to reveal the significant lags of these decomposed components. These lags are then used as inputs of neural network model to forecast the future values of each decomposed component. Finally, the forecasted components are combined together to form the electricity demand forecast. A case study has been reported in the paper by acquiring the data for the state of New South Wales, Australia. MAPE is used to validate the proposed model and the results show that the proposed method is promising for short term load forecasting.
Implementation Of Neural Network Based Electricity Load Forecasting
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This paper proposed a novel model for short term load forecast (STLF) in the electricity market. The prior electricity demand data are treated as time series. The model is composed of several neural networks whose data are processed using a wavelet technique. The model is created in the form of a simulation program written with MATLAB. The load data are treated as time series data. They are decomposed into several wavelet coefficient series using the wavelet transform technique known as Non-decimated Wavelet Transform (NWT). The reason for using this technique is the belief in the possibility of extracting hidden patterns from the time series data. The wavelet coefficient series are used to train the neural networks (NNs) and used as the inputs to the NNs for electricity load prediction. The Scale Conjugate Gradient (SCG) algorithm is used as the learning algorithm for the NNs. To get the final forecast data, the outputs from the NNs are recombined using the same wavelet technique. ...
A comparative study for Short Term Load Forecasting using ANN with and without Wavelet transform
Due to centralized power system and continuous varying nature of load it is very difficult to balance demand and generation at all time. It is due to the fact that generation can't be controlled with the same pace as load due to restriction in instantaneous change in input to power plant. In this paper a work is presented to forecast week ahead loadusing ANN model with wavelet transform signal processing which has outperform all previous method due to the exceptional nature of leaning in neural network. The used datasets in this work are based on the past weather records. To demonstrate the effectiveness of the proposed approach data of load from 132 KV substation of Rajgarh (Dhar), M.P. has been taken to forecast the daily peak load for the Indore City of Madhya Pradesh.The data used in this work is from 1 st Jan 2017 to 31 st Dec 2017. All input variables have per day peak readings, so a total of 365 samples of each parameter are used in study.
Wavelet-GA-ANN Based Hybrid Model for Accurate Prediction of Short-Term Load Forecast
2007 International Conference on Intelligent Systems Applications to Power Systems, 2007
This paper proposes a hybrid model developed through wiser integration of wavelet transforms, floating point GA and artificial neural networks for prediction of short-term load. The use of wavelet transforms has added the capability of capturing of both global trend and hidden templates in loads, which is otherwise very difficult to incorporate into the prediction model of ANN. Auto-configuring RBF networks are used for predicting the wavelet coefficients of the future loads. Floating point GA (FPGA) is used for optimizing the RBF networks. The use of GA optimized RBF network has added to the model the online prediction capability of short-term loads accurately. The performance of the proposed model is validated using Queensland electricity demand data from the Australian National Electricity Market. Results demonstrate that the proposed model is more accurate as compared to RBF only model.
Information, 2018
Electricity load forecasting plays a paramount role in capacity planning, scheduling, and the operation of power systems. Reliable and accurate planning and prediction of electricity load are therefore vital. In this study, a novel approach for forecasting monthly electricity demands by wavelet transform and a neuro-fuzzy system is proposed. Firstly, the most appropriate inputs are selected and a dataset is constructed. Then, Haar wavelet transform is utilized to decompose the load data and eliminate noise. In the model, a hierarchical adaptive neuro-fuzzy inference system (HANFIS) is suggested to solve the curse-of-dimensionality problem. Several heuristic algorithms including Gravitational Search Algorithm (GSA), Cuckoo Optimization Algorithm (COA), and Cuckoo Search (CS) are utilized to optimize the clustering parameters which help form the rule base, and adaptive neuro-fuzzy inference system (ANFIS) optimize the parameters in the antecedent and consequent parts of each sub-model. The proposed approach was applied to forecast the electricity load of Hanoi, Vietnam. The constructed models have shown high forecasting performances based on the performance indices calculated. The results demonstrate the validity of the approach. The obtained results were also compared with those of several other well-known methods including autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR). In our study, the wavelet CS-HANFIS model outperformed the others and provided more accurate forecasting.
Comparison of fuzzy time series, ANN and wavelet techniques for short term load forecasting
International Journal of Power Electronics and Drive Systems (IJPEDS), 2023
The present article presents the load forecasting for a power system (substation) load demands using techniques based on fuzzy time series (FTS), artificial neural network (ANN), and wavelet transform (WT). The mean absolute percentage error (MAPE), integral absolute error (IAE), integral of time multiplied error (ITAE), integral square error (ISE) along with integral time multiplied square error (ITSE) criteria are used for determining the performance indices and minimizing the error. From the investigations of the results obtained in the study, it is inferred that forecasting of electric load based on WT and ANN offers less error as compared to FTS. The suggested integrated model captures the useful properties of artificial neural networks and wavelet transforms in time series and is found to be accurate for real-time data.