Prediction of Energy Demand in Smart Grid using Hybrid Approach
| Autor: | Muralitharan Krishnan, Sangwoon Yun, Yoon Mo Jung |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Computer science 020209 energy 0208 environmental biotechnology 02 engineering and technology Energy consumption Service provider computer.software_genre 020801 environmental engineering Smart grid Moving average 0202 electrical engineering electronic engineering information engineering Electricity market Data mining Time series computer Energy (signal processing) |
| Zdroj: | 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC). |
| DOI: | 10.1109/iccmc48092.2020.iccmc-00055 |
| Popis: | Predicting the energy consumption to provide valuable decision information for service providers and customers is a challenging task on the electricity market. The combination of mathematical and ANN methods results in a precise method of energy prediction than a single mathematical or ANN approach for predicting time-series data starting with different applications. The combination of (linear) seasonal auto-regressive integrated moving average (SARIMA) and (nonlinear) long-short term memory (LSTM) models are adopted to construct an advanced hybrid SARIMA-LSTM model for forecasting the time series data. Compared to existing models such as SARIMA and LSTM, a combination of the SARIMA-LSTM technique produces more accurate predictions. The results of the simulation reveal that better prediction is achieved by the proposed hybrid model. Further, the prediction model explores detailed energy consumption patterns with seasonal properties which makes both the service provider and customer to reduce their energy generation and consumption cost, respectively. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|