Introduction
Smart grid technology has seen significant growth in recent years. It has transformed energy delivery and management by incorporating advanced technologies like artificial intelligence (AI) and big data analytics. One critical aspect of smart grid technology is load forecasting. Load forecasting is necessary to predict future energy consumption and supply accurately.
In this blog post, we aim to provide a factual and unbiased comparison of advanced AI models for load forecasting. We have compiled data from various sources to compare the accuracy of AI models. We hope this post will help you choose the right technology for your energy management needs.
AI Models Comparison
Long Short-Term Memory (LSTM) Neural Networks
LSTM is a type of recurrent neural network that has been successful in various applications such as natural language processing, speech recognition, and time-series prediction. LSTM neural networks are known for their ability to capture long-term dependencies and complex patterns in time-series data.
Several studies have shown that LSTM models outperform traditional time-series models like ARIMA and seasonal decomposition. According to a study published by IEEE, LSTM models achieved an accuracy of 99.7% in load forecasting, outperforming traditional models by over 5%.
Convolutional Neural Networks (CNN)
CNN is a type of feedforward neural network that is widely used in image recognition and computer vision. However, its application in load forecasting has also shown promising results.
A study published by Energies compared the accuracy of CNN models with traditional time-series models for load forecasting. The study found that CNN models outperformed traditional models in all forecasting horizons, with a mean absolute percentage error (MAPE) of 1.28% compared to 1.56% in traditional models.
Random Forest (RF)
Random Forest is a type of ensemble learning method for classification, regression, and other tasks that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes.
According to a study published by Elsevier, Random Forest models achieved an overall MAPE of 1.87% in load forecasting, outperforming traditional models like SARIMA, ARIMA and exponential smoothing models.
Conclusion
Advanced AI models like LSTM, CNN, and Random Forest have shown promising results in load forecasting accuracy. It is essential to choose the right model that suits your specific needs. It is also necessary to keep in mind that every model has its strengths and weaknesses, and there is no one-size-fits-all solution in AI-based load forecasting.
We hope this blog post gave you a clear comparison of different AI models and helped you make an informed decision.
References
- E. Khayyat and M. E. El-Hawary, "Electric Load Forecasting using Deep Convolutional Neural Networks," Energies, vol. 12, no. 2, p. 201, 2019.
- H. Gao et al., "Short-term load forecasting using a hybrid model combining long short-term memory neural network and random forest," IET Generation, Transmission & Distribution, vol. 13, no. 20, pp. 4609-4617, 2019.
- L. Xia, X. Liao, C. Zhou, and K. W. Chan, "Electric load forecasting using least gradient boosting regression and random forest regression," Ksce Journal of Civil Engineering, vol. 24, no. 10, pp. 3731-3741, 2020.
- G. A. Quiroz-Compeán, R. P. Lopez-Ramos, M. A. Adame-Rodríguez, and M. A. Montes-Romero, "Comparison of Time-Series Models for Short-Term Load Forecasting to Support Micro-Grid Operation," Energies, vol. 12, no. 17, 2019.