Accurate load forecasting of district heating systems (DHSs) is an essential guide to guaranteeing effective energy production, distribution, and rational utilization. Artificial neural networks have ...been extensively applied to heating energy prediction in DHS. Recently, a new time series prediction model namely Informer was proposed. This study proposes an Informer-based framework for DHS heating load forecasting. To explore the performance of Informer in heating load forecasting tasks, four forecasting models namely Autoregressive Integrated Moving Average model, Multilayer Perceptron, Recurrent Neural Network and Long Short-Term Memory network are established for comparison. The historical heating load, outdoor temperature, relative humidity, wind speed and air quality index of a DHS in Tianjin are used as the input characteristics to comprehensively assess the performance of these five forecasting strategies. The prediction results of the models are evaluated and visualized. The experimental results show that the Informer-based forecasting model can achieve the most accurate and stable predictions. Furthermore, a relative position encoding algorithm is introduced to enhance its generalization and robustness. Overall, the Informer-based framework can report satisfactory testing results. The prediction curve is fitted to the trend of temperature change which can play an excellent guiding role in heating dispatching.
•A new framework based on Informer is proposed for heating load forecasting of a DHS in Tianjin, China.•Informer is compared with other four popular prediction models namely ARIMA, MLP, RNN and LSTM.•The performance of Informer in heating load forecasting has been verified.•A relative position coding is introduced to improve the prediction ability of Informer.
This paper proposes a novel flexible pressure sensor based on carbon black (CB), carboxy-methyl cellulose (CMC), and gelatin. CMC and gelatin are both food-grade materials that are degradable, ...non-toxic, and environmentally friendly. Freeze drying is performed to obtain a sensor porous honeycomb structure that achieves not only a wide monitoring range from 0 to 140% strain, but also high sensitivity (maximum gauge factor of 12.5, which is higher than ordinary conductive CB composite systems). After 3000 repeated presses, the sensor remains unchanged and retains its high sensitivity. This stable sensor response is promising for long-term practical applications. The proposed sensor is applied to a speech recognition system and can distinguish the different components of a sentence, thereby achieving accurate speech recognition. It is also capable of monitoring human body movements, including joint flexion and finger movement. Finally, a sensor integrated network is constructed for application to a human–machine interface. The proposed CB sponge pressure sensor has important applications for medical wearable devices and smart wear.
Wind power forecast remains challenging owing to the unpredictable peculiarity of wind. The accuracy of wind power predictions is critical to the stability of the whole system. This research proposes ...a hybrid prediction model based on a temporal convolutional network and an Informer to increase the accuracy of wind power forecasting. The hidden temporal features in the dataset are first extracted using TCN, and the Informer is then employed to predict wind power. Additionally, a cutting-edge AdaBelief optimizer is used to boost prediction accuracy even more. The validity of the model is verified by comparing with other wind speed prediction methods. The findings reveal that the proposed model has the highest prediction accuracy and the best forecast effect.
•A hybrid wind power forecasting model based on TCN and Informer is proposed•The temporal convolutional network are uesd to extract the wind power feature.•The Adabelief optimizer is used to further improve the prediction results.•The model proposed in this paper achieves the best results on the four error evaluation metrics.
A Mach–Zehnder interferometer (MZI) based on the cascaded structure of a single-mode fiber (SMF) down-taper and thin-core fiber (TCF) is proposed and experimentally demonstrated. The down-taper acts ...as mode coupler to excite cladding modes. The extinction ratio of the transmission spectra can reach up to 20dB. By monitoring the wavelength shifts of interference dip1 and dip2, simultaneous measurement of refractive index (RI) and temperature can be achieved. The experimental results show that the wavelengths at interference dip1and dip2 have blue shifts with the increase of RI and red shifts with the increase of temperature. The RI sensitivities at interference dip1 and dip2 are -30.290 nm/RIU and -79.335nm/RIU, respectively. The temperature sensitivities at interference dip1 and dip2 are 0.065 nm/ºC and 0.053 nm/ºC, respectively. The proposed MZI exhibits the advantages of easy fabrication, high extinction ratio, low cost, and simultaneous measurement of RI and temperature, which will make a significant contribution to RI measurement.
•A Mach–Zehnder interferometer based on down-taper and thin-core fiber is proposed.•Temperature and refraction index can be measured simultaneously.•The temperature sensitivities of the dips are 0.065 nm/ºC and 0.053 nm/ºC.•The refractive index sensitivities of the dips are -30.290 nm/RIU and -79.335 nm/RIU.•Several interferometers with different lengths are studied.
An optical fiber sensor based on few-mode fiber and spherical structure is proposed and demonstrated. Temperature and refractive index can be measured simultaneously, since the interference spectrums ...between certain high core mode and different order cladding modes of the few-mode fiber have different sensitivities for the two parameters. The dips at 1526.4 nm and 1553.77 nm are chosen to measure the temperature and refractive index. The results of the experiment indicate that the temperature sensitivities of the dips are 0.059 nm/°C and 0.05 nm/°C, respectively. The refractive index sensitivities of the dips are −39.15 nm/RIU and −48.82 nm/RIU, respectively. And the temperature and the refractive index resolutions of the sensor are 0.95°C and 0.0012RIU, respectively. Simultaneous measurement of temperature and refractive index can be realized by the sensor structure. This fiber interferometer sensor can also be applied in other sensing fields and has good prospects.
•A sensor based on few-mode fiber and spherical structure is proposed.•Temperature and refractive index can be measured simultaneously.•The temperature sensitivities of the dips are 0.059 nm/°C and 0.05 nm/°C.•The refraction index sensitivities of the dips are −39.15 nm/RIU and −48.82 nm/RIU.•Sensors with different lengths and diameters of spherical structure are studied.
Given the continuous expansion of heating areas in recent years, the design of a precise and dependable district heating system (DHS) has become increasingly crucial. Traditional control decisions ...are made based on real-time environmental temperature feedback, often leading to uneven heating on the user side and affecting residents' comfort. This paper proposes an intelligent control strategy based on the deep reinforcement learning recurrent deterministic policy gradient (RDPG) algorithm for DHSs. To explore the control performance of the RDPG algorithm on DHS, we have meticulously modeled the pivotal components of the DHSs, namely plate heat exchangers, secondary heating pipe networks, and heat users. Moreover, taking into account the periodic factors in heating regulation, the traditional recurrent neural network (RNN) in the recurrent deterministic policy gradient (RDPG) algorithm has been replaced with the long short-term memory (LSTM) network. The proposed algorithm was trained using actual data from a heat exchange station in Tianjin and compared with reinforcement learning algorithms such as TD3, DPPO, DDPG, and A3C in terms of training rewards, effectiveness, and training stability. The results of the models are evaluated and visualized. Experimental results show that the proposed control method based on the RDPG algorithm, compared to other control schemes, can achieve the highest training reward and the most stable control performance, with an indoor temperature fluctuation range of only 0.1 °C.
Accurate heat load forecasting is an important issue to ensure the reliable and efficient operation of a district heating system. In this paper, a hybrid model that combines similar day (SD) ...selection and Deep Neural Networks (DNNs) to construct SD-DNNs model for short-term load forecasting (STLF) is presented. A new Euclidean Norm (EN) weighted by eXtreme Gradient Boosting (XGBoost) is used to evaluate the similarity between the forecasting day and historical days. In this EN, the outdoor temperature, wind force and day-ahead load are simultaneously considered. And eight features are chosen as inputs of the DNNs to predict the heat load. The Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Percentage Error (MPE) are used as accuracy evaluation indexes. The experimental results demonstrate that the SD-DNNs model can accurately forecast the heat load.
Accurate heat load prediction is a prerequisite for feed-forward control and on-demand heat supply in district heating system. However, considering that the experimental data used to train the ...prediction model are often not optimal or most energy efficient, accurate prediction is difficult to achieve effective energy-saving. This paper proposes a hybrid energy-saving prediction model that combines similar sample selection approach (SSA) and deep neural network. A new weighted Euclidean norm (WEN) is used to select suitable similar sample datasets, and a novel energy-saving strategy is proposed to reduce energy consumption. To make the prediction performance more stable, a low-pass filter is used to filter the prediction results. In the case study, real data from a heat exchange station in Tianjin are used to verify the prediction performance of the hybrid model for 1 test day, 3 test days, and 7 test days. Experimental results show that: (a) the proposed model is able to capture the change trend of heat load, with Pearson correlation coefficient of 0.971, 0.969, and 0.954 on different test days, respectively; (b) the proposed model is able to effectively reduce energy consumption, with energy-saving of 5.4%, 7.6%, and 4.8% on different test days, respectively.
