Abstract:
The increasing use of renewable energy resources in power systems worldwide has
led to the development of smart grids, which allow for bidirectional communication
between supply and demand. Accurate load forecasting is crucial for maximizing
the efficiency of these smart grids, as it allows for the switching between renewable
and other energy resources, reducing overall costs and transmission losses. Neural
network-based load forecasting models are performing better than traditional statistical
models. The hybridization of neural network-based models with optimized learning
algorithms is the focus of our research. In this work, we propose a novel approach
for load forecasting in smart grids. The approach includes four key components:
refactoring the smart grid dataset for load forecasting, gathering a highly relevant set of
parameters affecting domestic load, constructing four hybrid models with appropriate
tuning parameters, and comparative analysis of these models. We devise proper
format and structure the data used for load forecasting, ensuring that it is in the
best possible form for accurate predictions. We carefully select the most important
parameters for medium and short-term load forecasting, ensuring that the model is
able to take into account the most relevant factors. We use an empirical approach to
determine the best set of parameters for our models, ensuring that they are optimized
for accurate predictions.We have modelled three machine learning hybrid models with
ANN, hybridized with genetic algorithm, particle swarm optimization, and Levenberg-
Marquardt, along with one deep learning model RNN-LSTM. Our comparative results
show that ANN-GA and ANN-PSO performed better in medium-term forecast with accuracy rates of 93.7 and 98.26 percent, respectively. While ANN-LM and RNNLSTM
performed better in short-term forecast achieving accuracy rates of 97 and 99
percent, respectively. Our results demonstrate that our proposed approach is effective
in achieving highly accurate load forecasts, making it a valuable tool for optimizing the
performance of smart grids
