Energy forecasting has a vital role to play in smart grid (SG) systems. Managing efficient forecasting while ensuring the least possible prediction error is one of the main challenges posed in the grid. This paper provides a comprehensive review of state-of-the-art forecasting methods for SG systems.
Energy forecasting has a vital role to play in smart grid (SG) systems
involving various applications such as demand-side management, load shedding,
optimum dispatch, etc. Managing efficient forecasting while ensuring the least
possible prediction error is one of the main challenges posed in the grid
today, considering the uncertainty and granularity in the SG data. This paper
presents a comprehensive and application-oriented review of state-of-the-art
forecasting methods for SG systems considering the different models and
architectures. Traditional statistical and machine learning-based forecasting
methods are extensively investigated in terms of their applicability to energy
forecasting. In addition, the significance of hybrid methods and data
pre-processing techniques for better forecasting accuracy is also highlighted.
A comparative case study using the Victorian electricity consumption benchmark
and American electric power (AEP) datasets is conducted to analyze the
performance of different forecasting methods. The analysis demonstrates higher
accuracy of the recurrent neural network (RNN) and long-short term memory
(LSTM) methods when sample sizes are larger and hyperparameters are
appropriately tuned. Furthermore, hybrid methods such as CNN-LSTM are also
highly effective to deal with long sequences in energy data.