Long short-term memory (LSTM) models based on specialized deep neural network-based architecture have emerged as an important model for forecasting time-series. However, the literature does not provide clear guidelines for design choices, which affect forecasting performance. Such choices include the need for pre-processing techniques such as deseasonalization, ordering of the input data, network size, batch size, and forecasting horizon. We detail this in the context of short-term forecasting of global horizontal irradiance, an accepted proxy for solar energy. Particularly, short-term forecasting is critical because the cloud conditions change at a sub-hourly having large impacts on incident solar radiation. We conduct an empirical investigation based on data from three solar stations from two climatic zones of India over two seasons. From an application perspective, it may be noted that despite the thrust given to solar energy generation in India, the literature contains few instances of robust studies across climatic zones and seasons. The model thus obtained subsequently outperformed three recent benchmark methods based on random forest, recurrent neural network, and LSTM, respectively, in terms of forecasting accuracy. Our findings underscore the importance of considering the temporal order of the data, lack of any discernible benefit from data pre-processing, the effect of making the LSTM model stateful. It is also found that the number of nodes in an LSTM network, as well as batch size, is influenced by the variability of the input data.

Accurate short-term solar forecasting is challenging due to weather uncertainties associated with cloud movements. Typically, a solar station comprises a single prediction model irrespective of time and cloud condition, which often results in suboptimal performance. In the proposed model, different categories of cloud movement are discovered using K-medoid clustering. To ensure broader variation in cloud movements, neighboring stations were also used that were selected using a dynamic time warping (DTW)-based similarity score. Next, cluster-specific models were constructed. At the prediction time, the current weather condition is first matched with the different weather groups found through clustering, and a cluster-specific model is subsequently chosen. As a result, multiple models are dynamically used for a particular day and solar station, which improves performance over a single site-specific model. The proposed model achieved 19.74% and 59% less normalized root mean square error (NRMSE) and mean rank compared to the benchmarks, respectively, and was validated for nine solar stations across two regions and three climatic zones of India.

Wind flow can be highly unpredictable and suffer substantial fluctuations in speed and direction due to the shape and height of hills, mountains, and valleys, making accurate wind speed (WS) forecasting essential in complex terrain. Hourly WS data at 50 meters above ground, from MERRA-2, NASA (2015–2021), collected from five Indian wind stations for plain and complex terrain. This paper presents a novel and adaptive model for short-term WS forecasting. The paper’s key contributions are as follows: (a) The Partial Auto Correlation Function (PACF) is utilised to minimise the dimension of the set of Intrinsic Mode Functions (IMF), hence reducing training time; (b) The sample entropy (SampEn) was used to calculate the complexity of the reduced set of IMFs. Since a particular Deep Learning (DL) model-feature-combination was selected based on complexity, the proposed method is adaptive; (c) A novel bidirectional feature- LSTM framework for complicated IMFs has been suggested, resulting in improved forecasting accuracy; (d) The proposed model shows 55.94% superior forecasting performance compared to the persistence, hybrid, Ensemble empirical mode decomposition (EEMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD)-based DL models. It has achieved the lowest prediction variance between simple and complex terrain at 0.70%, ensuring robust forecasting performance. Dimension reduction of IMF’s and complexity-based model-feature selection helps reduce the training time by 68.77%, additionally forecasting quality is improved by 58.58% on average. These benefits highlight the model’s adaptability, effectiveness, and resilience in addressing WS forecasting challenges on complex terrain.