In an increasingly open electricity market environment, short-term load forecasting (STLF) can ensure the power grid to operate safely and stably, reduce resource waste, power dispatching, and provide technical support for demand-side response. Recently, with the rapid development of demand side response, accurate load forecasting can better provide demand side incentive for regional load of prosumer groups. Traditional machine learning prediction and time series prediction based on statistics failed to consider the non-linear relationship between various input features, resulting in the inability to accurately predict load changes. Recently, with the rapid development of deep learning, extensive research has been carried out in the field of load forecasting. On this basis, a feature selection algorithm based on random forest is first used in this paper to provide a basis for the selection of the input features of the load forecasting model. After the input features are selected, a hybrid neural network STLF algorithm based on multi-model fusion is proposed, of which the main structure of the hybrid neural network is composed of convolutional neural network and bidirectional gated recurrent unit (CNN-BiGRU). The input data is obtained by using long sliding time windows of different steps, then multiple CNN-BiGRU models are trained respectively. The forecasting results of multiple models are averaged to get the final forecasting load value. The load datasets come from a region in New Zealand and a region in Zhejiang, China, are used as load forecast examples. Finally, a variety of load forecasting algorithms are introduced for comparison. The experimental results show that our method has a higher accuracy than comparison models.

We report a universal strategy consisting of I1/I2 and I2/I1 parts to enhance the SA for temperature sensing applications.

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Oxide upconversion (UC) materials show great potential for temperature detection application due to its excellent thermostability. The CaTiSiO5 (CTS) host with excellent physicochemical properties is difficult to be prepared owing to the emergence of stable CaTiO3 phase. Herein, we focus on the synthesis of pure CTS phase and the investigation of UC properties of Er3+ and Er3+ - Yb3+ doped CTS phosphors. Three diverse routes are adopted toward the synthesis of pure CTS phase, which demonstrates that shielding the Ca2+ in crystal lattice is vital for achieving this target. The optimal calcined temperature at 1270°C is confirmed by XRD, morphology change and EDX results. UC properties of color-emitting, optimal Er3+/Yb3+ concentrations as well as temperature behavior are revealed. The CTS:1%Er3+/1%Yb3+ phosphor with bright green and red emissions is obtained and low Er3+/Yb3+ dopant concentrations are caused by the unequal substitutions between Er3+/Yb3+ and Ca2+ ions. High SA value as well as its maximal value of 52.3 × 10−4 K−1 coupled with the SR of 46.3 × 10−4 K−1 at 413 K are gained and the SA value with small fluctuation were achieved in the CTS:1%Er3+/1%Yb3+ sample. These results suggest that the CTS:1%Er3+/1%Yb3+ may be a candidate for temperature detection application.