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Northern China is vigorously promoting cogeneration and clean heating technologies. The accurate prediction of building energy consumption is the basis for heating regulation. In this paper, the daily, weekly, and annual periods of building energy consumption are determined by Fourier transformation. Accordingly, a period-based neural network (PBNN) is proposed to predict building energy consumption. The main innovation of PBNN is the introduction of a new data structure, which is a time-discontinuous sliding window. The sliding window consists of the past 24 h, 24 h for the same period last week, and 24 h for the same period the previous year. When predicting the building energy consumption for the next 1 h, 12 h, and 24 h, the prediction errors of the PBNN are 2.30%, 3.47%, and 3.66% lower than those of the traditional sliding window PBNN (TSW-PBNN), respectively. The training time of PBNN is approximately half that of TSW-PBNN. The time-discontinuous sliding window reduces the energy consumption prediction error and neural network model training time. [ABSTRACT FROM AUTHOR]

The application of intelligent equipment and technologies such as robots and unmanned vehicles is an important part of the construction of intelligent mines, and has become China's national coal energy development strategy and the consensus of the coal industry. Environment perception and instant positioning is one of the key technologies destined to realize unmanned and autonomous navigation in underground coal mines, and simultaneous location and mapping (SLAM) is an effective method of deploying this key technology. The underground space of a coal mine is long and narrow, the environment is complex and changeable, the structure is complex and irregular, and the lighting is poor. This is a typical unstructured environment, which poses a great challenge to SLAM. This paper summarizes the current research status of underground coal mine map construction based on visual SLAM and Lidar SLAM, and analyzes the defects of the LeGO-LOAM algorithm, such as loopback detection errors or omissions. We use SegMatch to improve the loopback detection module of LeGO-LOAM, use the iterative closest point (ICP) algorithm to optimize the global map, then propose an improved SLAM algorithm, namely LeGO-LOAM-SM, and describe its principle and implementation. The performance of the LeGO-LOAM-SM was also tested using the KITTI dataset 00 sequence and SLAM experimental data collected in two coal mine underground simulation scenarios, and the performance indexes such as the map construction effect, trajectory overlap and length deviation, absolute trajectory error (ATE), and relative pose error (RPE) were analyzed. The results show that the map constructed by LeGO-LOAM-SM is clearer, has a better loopback effect, the estimated trajectory is smoother and more accurate, and the translation and rotation accuracy is improved by approximately 5%. This can construct more accurate point cloud map and low drift position estimation, which verifies the effectiveness and accuracy of the improved algorithm. Finally, to satisfy the navigation requirements, the construction method of a two-dimensional occupancy grid map was studied, and the underground coal mine simulation environment test was carried out. The results show that the constructed raster map can effectively filter out outlier noise such as dynamic obstacles, has a mapping accuracy of 0.01 m, and the required storage space compared with the point cloud map is reduced by three orders of magnitude. The research results enrich the SLAM algorithm and implementation in unstructured environments such as underground coal mines, and help to solve the problems of environment perception, real-time positioning, and the navigation of coal mine robots and unmanned vehicles. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of the multiple load forecasting of a regional integrated energy system, a short-term multiple load forecasting model based on the quantum weighted GRU and multi-task learning framework is proposed in this paper. Firstly, correlation analysis is carried out using a maximum information coefficient to select the input of the model. Then, a multi-task learning architecture is constructed based on the quantum weighted GRU neural network, and the coupling information among multiple loads is learned through the sharing layer in order to improve the prediction accuracy of multiple loads. Finally, the PSO algorithm is used to optimize the parameters of the quantum weighted GRU. The simulation data of a regional integrated energy system in northern China are used to predict the power and cooling loads on summer weekdays and rest days, and the results show that, compared with the LSTM, GRU and single task learning QWGRU models, the proposed model is more effective in the multiple load forecasting of a regional integrated energy system. [ABSTRACT FROM AUTHOR]

The accuracy of annual electric load forecasting plays an important role in the economic and social benefits of electric power systems. The least squares support vector machine (LSSVM) has been proven to offer strong potential in forecasting issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. As a novel meta-heuristic and evolutionary algorithm, the fruit fly optimization algorithm (FOA) has the advantages of being easy to understand and fast convergence to the global optimal solution. Therefore, to improve the forecasting performance, this paper proposes a LSSVM-based annual electric load forecasting model that uses FOA to automatically determine the appropriate values of the two parameters for the LSSVM model. By taking the annual electricity consumption of China as an instance, the computational result shows that the LSSVM combined with FOA (LSSVM-FOA) outperforms other alternative methods, namely single LSSVM, LSSVM combined with coupled simulated annealing algorithm (LSSVM-CSA), generalized regression neural network (GRNN) and regression model. [ABSTRACT FROM AUTHOR]

The smart district heating system (SDHS) is an important element of the construction of smart cities in Northern China; it plays a significant role in meeting heating requirements and green energy saving in winter. Various Internet of Things (IoT) sensors and wireless transmission technologies are applied to monitor data in real-time and to form a historical database. The accurate prediction of heating loads based on massive historical datasets is the necessary condition and key basis for formulating an optimal heating control strategy in the SDHS, which contributes to the reduction in the consumption of energy and the improvement in the energy dispatching efficiency and accuracy. In order to achieve the high prediction accuracy of SDHS and to improve the representation ability of multi-time-scale features, a novel short-term heating load prediction algorithm based on a feature fusion long short-term memory (LSTM) model (FFLSTM) is proposed. Three characteristics, namely proximity, periodicity, and trend, are found after analyzing the heating load data from the aspect of the hourly time dimension. In order to comprehensively utilize the data's intrinsic characteristics, three LSTM models are employed to make separate predictions, and, then, the prediction results based on internal features and other external features at the corresponding moments are imported into the high-level LSTM model for fusion processing, which brings a more accurate prediction result of the heating load. Detailed comparisons between the proposed FFLSTM algorithm and the-state-of-art algorithms are conducted in this paper. The experimental results show that the proposed FFLSTM algorithm outperforms others and can obtain a higher prediction accuracy. Furthermore, the impact of selecting different parameters of the FFLSTM model is also studied thoroughly. [ABSTRACT FROM AUTHOR]

The frequency of typhoons in China has gradually increased, resulting in serious damage to low-voltage power grid lines. Therefore, it is of great significance to study the influencing factors and predict the amount of damage, which contributes to enhancing wind resistance and improving the efficiency of repairs. In this paper, 18 influencing factors with a correlation degree higher than 0.75 are selected by grey correlation analysis, and then converted into six common factors by factor analysis. Additionally, an extreme learning machine optimized by an improved gravitational search algorithm, hereafter referred to as IGSA-ELM, is established to predict the damage caused to the low-voltage lines by typhoons and verify the effectiveness of the factor analysis. The results reveal that the six common factors generated by factor analysis can effectively improve the prediction accuracy and the fitting effect of IGSA-ELM is better than those of the extreme learning machine (ELM) and the extreme learning machine based on particle swarm optimization (PSO-ELM). Finally, this article proposes valid policy recommendations to improve the anti-typhoon capacity and repair efficiency of the low-voltage lines in Guangdong Province. [ABSTRACT FROM AUTHOR]