In this work, we report an optoelectronic synapse based on the Bi2O2Se/Cs3Cu2I5 heterojunction formed by consequently depositing Bi2O2Se and Cs3Cu2I5 thin films by chemical vapor deposition methods. The fabricated Au/Bi2O2Se/Cs3Cu2I5/Au devices exhibit the light-tunable photoresponse under visible light illumination. Synaptic functions including paired-pulse facilitation, the transition from short-term plasticity to long-term plasticity, associative learning and optical encoding, are conveniently reproduced on one single synaptic device by utilizing the light-tunable evolution of photocurrent. Furthermore, the front-end image preprocessing to enhance the accuracy and efficiency of image recognition was demonstrated based on the constructed device array (5 × 7). This indicates the potential application of Bi2O2Se/Cs3Cu2I5 optoelectronic synapses for the forthcoming neuromorphic computing.

Accurate power load forecasting is critical to achieving the sustainability of energy management systems. However, conventional prediction methods suffer from low precision and stability because of crude modules for predicting short-term and medium-term loads. To solve such a problem, a Combined Modeling Power Load-Forecasting (CMPLF) method is proposed in this work. The CMPLF comprises two modules to deal with short-term and medium-term load forecasting, respectively. Each module consists of four essential parts including initial forecasting, decomposition and denoising, nonlinear optimization, and evaluation. Especially, to break through bottlenecks in hierarchical model optimization, we effectively fuse the Nonlinear Autoregressive model with Exogenous Inputs (NARX) and Long-Short Term Memory (LSTM) networks into the Autoregressive Integrated Moving Average (ARIMA) model. The experiment results based on real-world datasets from Queensland and China mainland show that our CMPLF has significant performance superiority compared with the state-of-the-art (SOTA) methods. CMPLF achieves a goodness-of-fit value of 97.174% in short-term load prediction and 97.162% in medium-term prediction. Our approach will be of great significance in promoting the sustainable development of smart cities.