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Load forecasting plays a crucial role in mitigating risks for utilities by predicting future
usage of commodity markets transmission or supplied by the utility. To achieve this, various techniques
such as price elastic demand, climate and consumer response, load analysis, and sustainable
energy generation predictive modelling are used. As both supply and demand fluctuate, and weather
and power prices can rise significantly during peak periods, accurate load forecasting becomes
critical for utilities. By providing brief demand forecasts, load forecasting can assist in estimating
load flows and making decisions that prevent overloading. Therefore, load forecasting is crucial in
helping electric utilities make informed decisions related to power, load switching, voltage regulation,
switching, and infrastructure development. Forecasting is a methodology used by electricity
companies to forecast the amount of electricity or power production needed to maintain constant
supply as well as load demand balance. It is required for the electrical industry to function properly.
The smart grid is a new system that enables electricity providers and customers to communicate
in real-time. The precise energy consumption sequence of the consumers is required to enhance the
demand schedule. This is where predicting the future comes into play. Forecasting future power
system load (electricity consumption) is a critical task in providing intelligence to the power grid.
Accurate forecasting allows utility companies to allocate resources and assume system control in
order to balance the same demand and availability for electricity. In this article, a study on load
forecasting algorithms based on deep learning, machine learning, hybrid methods, bio-inspired
techniques, and other techniques is carried out. Many other algorithms based on load forecasting
are discussed in this study. Different methods of load forecasting were compared using three performance
indices: RMSE (Root Mean Square Error), MAE (Mean Absolute Error), MAPE (Mean
Absolute Percentage Error), and Accuracy. Machine learning-based techniques showed a reduction
of 9.17% in MAPE, 0.0429% in RMSE, and 5.23% in MSE, and achieved 90% accuracy. Deep
learning-based techniques resulted in a 9.61% decrease in MAPE and achieved 91% accuracy. Bioinspired
techniques provided a reduction of 9.66% in MAPE, 0.026% in RMSE, and 5.24% in
MSE, and achieved 95% accuracy. These findings concluded that optimization techniques are more
encouraging in predicting load demand and, as a result, can represent a reliable decision-making
tool.