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In the solar power industry, irradiance forecasts are needed for planning, scheduling, and managing of photovoltaic power plants and grid-combined generating systems. A widely used method is artificial intelligence (AI), in particular, artificial neural networks, which can be trained over both historical values of irradiance and meteorological variables such as temperature, humidity, wind speed, pressure, and precipitation. In this paper, a novel version of the gated recurrent unit (GRU) method is combined with weather forecasts in order to predict solar irradiance. This method is used to forecast irradiance over a horizon of 24 h. Experiments show that the proposed method is able to outperform other AI methods. In particular, GRU using weather forecast data reduces the root mean squared error by 23.3% relative to a backpropagation neural network and 11.9% relative to a recurrent neural network. Compared to long short-term memory, the training time is reduced by 36.6%. Compared to persistence, the improvement in the forecast skill of the GRU is 42.0%. In summary, GRU is a promising technology which can be used effectively in irradiance forecasting.

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Structural metamaterial with negative Poisson's ratio often has low stiffness due to its porous structure. For engineering applications, the various strategies have been developed to enhance its stiffness but also weaken the negative Poisson effect. In this work, we propose a 3D reinforced re-entrant structure (RRS) by adding arrow structures to the classical re-entrant structure (RS), which is fabricated by photocuring 3D printing of photosensitive resin. The structure-properties correlative mechanism of RRS is systematically analyzed by combining experiment (quasi-static compression experiment) and finite element simulation (ABAQUS). It is found that the equivalent elastic modulus and negative Poisson's ratio of RRS can be tuned by various structural parameters (i.e. the thickness ratio η, the length ratio of the slant rod α, the length ratio of the vertical rod β, and the angle θ of the re-entrant cells). By optimizing the structure parameters, the equivalent elastic modulus of RRS with the negative Poisson’s ratio of -0.28 can be significantly increase to 13.67MPa, which is 12.32 times larger than that of RS with the same negative Poisson’s ratio. Due to the flexible design of stiffness and negative Poisson ratio, our proposed 3D reinforced re-entrant structural metamaterial provides a potential way for designing engineering materials with desirable performance.