Adaptive weighted dynamic time warping clustering with a deep codec model for accurate photovoltaic power prediction

As distributed photovoltaic (PV) systems increasingly integrate with smart grids, accurate ultra-short-term power forecasting is critical for optimizing grid dispatch and maintaining grid stability. However, the intermittency of solar irradiance and the complex multivariate temporal correlations in PV operational data present notable challenges to conventional forecasting models. Such models frequently fail to characterize nonlinear dynamics and effectively capture multiscale features of PV output. To mitigate these limitations, this study proposes a hybrid framework integrating adaptive feature-weighted dynamic time warping (AFDTW) clustering with an improved temporal convolutional network-long short-term memory (ITCN-LSTM) encoder-decoder. First, the AFDTW clustering algorithm employs an adaptive weighting mechanism based on Markov distance to prioritize high-variance features, enabling the capture of subtle patterns in PV output. A pruning strategy is additionally incorporated to reduce computational complexity. Subsequently, the clustered data are input into the ITCN-LSTM model. The ITCN leverages asymmetric causal and spatial convolutions to complex multivariate temporal correlations, thereby modeling geographic correlations. Meanwhile, the LSTM decoder captures long-term trends via its gated memory units. This architecture effectively bridges the gap between static, history-based models and real-time meteorological dynamics, facilitating reliable predictions under diverse weather conditions. Empirical evaluations using a real dataset from a 1.8-MW PV plant in Australia show that the proposed method outperforms the benchmark models across multiple forecasting horizons (5 min to 4 h). The proposed method significantly improves PV power-forecasting accuracy and supports more effective and reliable utilization of solar energy resources.