A short-term hybrid forecasting model for time series electrical-load data using random forest and bidirectional long short-term memory

In the presence of deregulated electric industry, load forecasting is more demanded than ever to ensure execution applications such as energy generation, pricing decisions, resource procurement, and infrastructure development. This paper presents a hybrid machine learning model for short-term (STLF) by applying random forest bidirectional long memory acquire benefits both methods. experimental evaluation, we used Bangladeshi electricity consumption dataset 36 months. The provides comparative study between proposed state-of-art models using performance metrics, loss analysis, prediction plotting. Empirical results demonstrate that shows better standard exhibiting accurate forecast results.