Improving the accuracy of very-short-term (VST) photovoltaic (PV) power generation prediction can effectively enhance the quality of operational scheduling of PV power plants, and provide a reference for PV maintenance and emergency response. In this paper, the effects of different meteorological factors on PV power generation as well as the degree of impact at different time periods are analyzed. Secondly, according to the characteristics of radiation coordinate, a simple radiation classification coordinate (RCC) method is proposed to classify and select similar time periods. Based on the characteristics of PV power time-series, the selected similar time period dataset (include power output and multivariate meteorological factors data) is reconstructed as the training dataset. Then, the long short-term memory (LSTM) recurrent neural network is applied as the learning network of the proposed model. The proposed model is tested on two independent PV systems from the Desert Knowledge Australia Solar Centre (DKASC) PV data. The proposed model achieving mean absolute percentage error of 2.74–7.25%, and according to four error metrics, the results show that the robustness and accuracy of the RCC-LSTM model are better than the other four comparison models.

ZnO nanowires (NWs) were grown on Si(100) substrates at 975 °C by a vapor-liquid-solid method with ~2 nm and ~4 nm gold thin films as catalysts, followed by an argon plasma treatment for the as-grown ZnO NWs. A single ZnO NW–based memory cell with a Ti/ZnO/Ti structure was then fabricated to investigate the effects of plasma treatment on the resistive switching. The plasma treatment improves the homogeneity and reproducibility of the resistive switching of the ZnO NWs, and it also reduces the switching (set and reset) voltages with less fluctuations, which would be associated with the increased density of oxygen vacancies to facilitate the resistive switching as well as to average out the stochastic movement of individual oxygen vacancies. Additionally, a single ZnO NW–based memory cell with self-rectification could also be obtained, if the inhomogeneous plasma treatment is applied to the two Ti/ZnO contacts. The plasma-induced oxygen vacancy disabling the rectification capability at one of the Ti/ZnO contacts is believed to be responsible for the self-rectification in the memory cell.

Nanomaterial-based memristors with analog resistive switching properties are used in the study of electronic synapses, providing information on both nanoscale device physics and low-power neuromorphic computing applications. Here, a memristor based on individual ZnO nanowires is prepared to study synaptic learning rules. Hebbian plasticity modulation is achieved with the co-application of pre- and post-synaptic spikes by tuning the temporal difference, spike frequency and voltage amplitude. Additionally, synaptic saturation is observed to stabilize the growth of synaptic weights. Plasma treatment of the memristors was performed to investigate its effects on synaptic plasticity and conductance modulation linearity during resistive switching. Plasma treatment allowed gradual conductance modulation of the memristor to be obtained, with improved conductance modulation linearity, suggesting that the memristor is capable of implementing synaptic plasticity to serve learning and memory. It was observed that the plasma treatment could also extend synaptic weight changes (Δ w ) to enhance learning capability and accelerate the learning speed of the electronic synapse, which might open up a route for modifying the characteristics of an electronic synapse. Synaptic learning and forgetting behavior are effectively simulated with re-learning of forgotten information at a much faster rate.

A p-i-n junction diode based on the selectively doped single-walled carbon nanotube (SWCNT) had been investigated, in which two opposite ends of individual SWCNT channel were doped into the p- and n-type SWCNT respectively while the middle segment of SWCNT was kept as the intrinsic. The symmetric and asymmetric contacts were used to fabricate the p-i-n junction diodes respectively and studied the effect of the contact on the device characteristics. It was shown that a low reverse saturation current of ~20 pA could be achieved by these both diodes. We found that the use of the asymmetric contact can effectively improve the performance of the p-i-n diode, with the rectification ratio enhanced from ~10² for the device with the Au/Au symmetric contact to >10³ for the one with the Pd/Al asymmetric contact. The improvement of the device performance by the asymmetric-contact structure was attributed to the decrease of the effective Schottky-barrier height at the contacts under forward bias, increasing the forward current of the diode. The p-i-n diode with asymmetric contact also had a higher rectification ratio than its counterpart before doping the SWCNT channel, which is because that the p-i-n junction in the device decreased the reverse saturated current.

Owing to the clean, inexhaustible and pollution-free, solar energy has become a powerful means to solve energy and environmental problems. However, photovoltaic (PV) power generation varies randomly and intermittently with respect to the weather, which bring the challenge to the dispatching of PV electrical power. Thus, power forecasting for PV power generation has become one of the key basic technologies to overcome this challenge. The paper presents a grey relational analysis (GRA) and long short-term memory recurrent neural network (LSTM RNN) (GRA-LSTM) model-based power short-term forecasting of PV power plants approach. The GRA algorithm is adopted to select the similar hours from history dataset, and then the LSTM NN maps the nonlinear relationship between the multivariate meteorological factors and power data. The proposed model is verified by using the dataset of the PV systems from the Desert Knowledge Australia Solar Center (DKASC). The prediction results of the method are contrasted with those obtained by LSTM, grey relational analysis-back propagation neural network (GRA-BPNN), grey relational analysis-radial basis function neural network (GRA-RBFNN) and grey relational analysis-Elman neural network (GRA-Elman), respectively. Results show an acceptable and robust performance of the proposed model.