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Short-term load forecasting is viewed as one promising technology for demand prediction
under the most critical inputs for the promising arrangement of power plant units. Thus, it is
imperative to present new incentive methods to motivate such power system operations for
electricity management. This paper proposes an approach for short-term electric load forecasting
using long short-term memory networks and an improved sine cosine algorithm called MetaREC.
First, using long short-term memory networks for a special kind of recurrent neural network, the
dispatching commands have the characteristics of storing and transmitting both long-term and
short-term memories. Next, four important parameters are determined using the sine cosine
algorithm base on a logistic chaos operator and multilevel modulation factor to overcome the
inaccuracy of long short-term memory networks prediction, in terms of the manual selection of
parameter values. Moreover, the performance of the MetaREC method outperforms others with
regard to convergence accuracy and convergence speed on a variety of test functions. Finally, our
analysis is extended to the scenario of the MetaREC_long short-term memory with back
propagation neural network, long short-term memory networks with default parameters, long
short-term memory networks with the conventional sine-cosine algorithm, and long short-term
memory networks with whale optimization for power load forecasting on a real electric load
dataset. Simulation results demonstrate that the multiple forecasts with MetaREC_long short-term
memory can effectively incentivize the high accuracy and stability for short-term power load
forecasting.