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Day-ahead load forecasting plays a crucial role in operation and management of power systems. Weather conditions have a significant impact on daily load profile, hence, it follows an almost similar pattern within a season. However, it varies markedly across the seasons. Existing literature on load forecasting adopts a very casual approach in considering the seasonality, based on either calendar month or some meteorological parameter, which is inconsistent and inaccurate, especially during transition periods, thus leading to high forecasting errors. This paper proposes a novel seasonal segmentation approach for day-ahead load forecasting that uses multiple bidirectional Long Short Term Memory (LSTM) networks. An index has been derived from various weather parameters that govern the selection of seasonal models for forecasting purposes. Weighted output from multiple seasonal models is also possible in special cases for better forecasting accuracy. The proposed seasonal segmentation approach avoids the need for frequent model retraining and results in a better forecast accuracy with a relatively simple LSTM structure. The performance of the proposed method has been validated and compared on the actual load data of Madhya Pradesh state (MP), India. The improved results suggest that the proposed approach can be applied reliably for load scheduling applications. • An encoder-decoder Bi-LSTM model with attention is developed to train the models. • An adaptive method for accurate identification of seasonal duration is proposed. • An index based selective and weighted method is developed for optimized forecasts. • Dynamic Time Warping (DTW) is used to adaptively select the forecast models. [ABSTRACT FROM AUTHOR]