Forecasting Electricity Load With Hybrid Scalable Model Based on Stacked Non Linear Residual Approach
| Autor: | Om Prakash Vyas, Raghav Tayal, Pankaj Pandey, A. K. Sinha, Aamod Vyas |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Economics and Econometrics
Mean squared error Computer science long short term memory convolutional neural network Energy Engineering and Power Technology computer.software_genre vector auto regression Convolutional neural network General Works Vector autoregression Moving average Time series electrical load forecasting business.industry Renewable Energy Sustainability and the Environment Deep learning Fuel Technology Autoregressive model Multilayer perceptron Data mining Artificial intelligence time series business computer |
| Zdroj: | Frontiers in Energy Research, Vol 9 (2021) Frontiers in Energy Research |
| ISSN: | 2296-598X |
| DOI: | 10.3389/fenrg.2021.720406 |
| Popis: | Power has totally different attributes than other material commodities as electrical energy stockpiling is a costly phenomenon. Since it should be generated when demanded, it is necessary to forecast its demand accurately and efficiently. As electrical load data is represented through time series pattern having linear and non-linear characteristics, it needs a model that may handle this behavior well in advance. This paper presents a scalable and hybrid approach for forecasting the power load based on Vector Auto Regression (VAR) and hybrid deep learning techniques like Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN). CNN and LSTM models are well known for handling time series data. The VAR model separates the linear pattern in time series data, and CNN-LSTM is utilized to model non-linear patterns in data. CNN-LSTM works as CNN can extract complex features from electricity data, and LSTM can model temporal information in data. This approach can derive temporal and spatial features of electricity data. The experiment established that the proposed VAR-CNN-LSTM(VACL) hybrid approach forecasts better than more recent deep learning methods like Multilayer Perceptron (MLP), CNN, LSTM, MV-KWNN, MV-ANN, Hybrid CNN-LSTM and statistical techniques like VAR, and Auto Regressive Integrated Moving Average (ARIMAX). Performance metrics such as Mean Square Error, Root Mean Square Error, and Mean Absolute Error have been used to evaluate the performance of the discussed approaches. Finally, the efficacy of the proposed model is established through comparative studies with state-of-the-art models on Household Power Consumption Dataset (UCI machine learning repository) and Ontario Electricity Demand dataset (Canada). |
| Databáze: | OpenAIRE |
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