Kernel Density Estimation Method Research Articles

Research on the spatiotemporal evolution and driving mechanism of coupling coordinating between green transition of urban land use and urban land use efficiency: a case study of the Yangtze River Delta Region in China.

Analyzing the spatiotemporal characteristics and driving mechanisms of the coupling coordination between the Green Transition of Urban Land Use and urban land use efficiency can help explore the future development direction of sustainable land use in cities. This paper constructs a theoretical framework for the coupling coordination between Green Transition of Urban Land Use and urban land use efficiency. We use several models, including the super-efficiency slack-based model, the coupling coordination degree model, the non-parametric kernel density estimation method, exploratory spatial data analysis, and the geographically and temporally weighted regression model to examine the real level of Green Transition of Urban Land Use and urban land use efficiency in the Yangtze River Delta region from 2003 to 2020. Based on this, we investigate the spatiotemporal evolution characteristics and driving mechanisms of the two coupling coordination processes. The study found that (1) from 2003 to 2020, the overall trend of the coupling coordination between Green Transition of Urban Land Use and urban land use efficiency in the Yangtze River Delta region tended to be coordinated and developed, but still at a primary coordination level, with sufficient room for improvement in the future. (2) The coupling coordination level of each city in the Yangtze River Delta region from 2003 to 2020 showed obvious spatial non-equilibrium and correlation characteristics, and overall dynamic polarization effects were exhibited during the study period; the spatial pattern of high-value areas showed a regularity of prioritizing Shanghai and Zhejiang Province, gradually penetrating into Jiangsu Province and Anhui Province. (3) Economic and social factors have a positive influence on the degree of coupling coordination; natural factors and policy factors have a predominantly negative influence on the degree of coupling coordination. Research conclusions include establishing a regional collaborative development mechanism, utilizing the spatial spillover effect of leading cities; emphasizing science, education, and culture, strengthening the introduction of scientific and technological talents, increasing fiscal inputs, raising the level of economic development, and further expanding the driving effect of economic and social factors; and optimizing the layout of urban and rural construction land, developing urban land in an orderly manner, appropriately strengthening environmental regulation, thereby suppressing the negative effects caused by natural and policy factors.

Spatial and temporal evolution of the eco-efficiency of cultivated land use in the region around Beijing–Tianjin based on the Super-EBM model

The eco-efficiency of cultivated land use (ECLU) is an important indicator for ecological civilization construction in China. Exploring the spatiotemporal dynamic evolution of the ECLU is helpful for the sustainable use of arable land, ensuring food security and ecological security. However, previous studies have mostly focused on the use of a slacks-based measure (SBM) model for ECLU measurement, ignoring the more accurate epsilon-based measure (EBM) model. Therefore, in this study, first, we explored the conceptual framework of the ECLU, and then, based on 2005–2020 panel data covering the counties in the region around Beijing and Tianjin, we investigated the spatial and temporal evolution of the ECLU by using the Super-EBM model, kernel density estimation method, and spatial Markov chain model. The results show the following: 1) From 2005 to 2020, the ECLU in the region around Beijing and Tianjin displayed an increasing state, but the average value was only 0.55. 2) The temporal evolution of the ECLU has gradually polarized, and the internal gap has widened, but it tends to stabilize. 3) The ECLU in the region around Beijing and Tianjin was more inclined to remain the same, and there was a “club convergence” phenomenon, which was meaningfully affected by the background of neighboring areas. In this article, we emphasize the significance of the super‐efficient EBM model in measuring the ecological efficiency of arable land use, and discuss the spatiotemporal evolution of the ecological efficiency of arable land use at a deeper level. In light of local conditions, the government should reasonably formulate a path to optimize the ECLU, strengthen the linkage with surrounding cities, and bring into play the positive spillover effect.

Urban resilience in China's eight urban agglomerations: evolution trends and driving factors.

Improving urban resilience (UR) and enhancing urban anti-risk ability are important foundations for promoting the high-quality development of new urbanization. This research employs the time-varying entropy method to evaluate the resilience level of 138 cities within China's eight urban agglomerations (UAs) between 2005 and 2019. Additionally, the Dagum Gini coefficient and the kernel density estimation method are utilized to examine the spatial disparities and distribution dynamics of UR across the eight UAs. The results of this investigation indicate that (1) the collective UR performance of the eight UAs has experienced an upward trend. However, a notable spatial disparity exits, which is primarily attributed to the differences among the UAs. (2) The overall UR development of the eight UAs has a certain gradient effect, and the UR within each UA has different degrees of polarization characteristics. (3) For the eight UAs as a whole, per capita savings deposits, capitalization of foreign capital, and per capita fiscal expenditure are the three most important driving factors. Within each UA, there was heterogeneity in the main influencing factors. The interplay between any two factors amplifies their individual driving effects on the spatial differentiation of UR.

Modeling and application of ship traffic conflicts in nearshore waters with heavy traffic

Ensuring the safety of ship navigation is essential to support the sustainable development of the shipping industry, and maritime safety analysis has thus received much attention in the field of shipping. Traditionally, the safety evaluation is mostly performed with the help of accident data, which is thus limited by the insufficient amount of accident data and affects the evaluation effect. In order to overcome this limitation, a ship traffic conflict model is established, which consists of three sub-models: multi-risk ship domain model, ship traffic conflict preliminary screening model and ship traffic conflict detection model. The multi-risk ship domain model is the key foundation, combining the traditional ship domain model with the water traffic conflict severity model to fully consider the severity of ship traffic conflict. To validate the proposed model, three ships in the study waters are selected as the own ships, and the severity of traffic conflicts formed with their target ships within a certain conflict time range are analyzed separately. Then, the conflict datasets obtained from the training of the ship traffic conflict model are used to analyze the traffic conflict situation in the heavily trafficked waters near Tianjin Port. Specifically, the spatial distribution characteristics are obtained using the kernel density estimation (KDE) method and the water grid method, and the temporal distribution characteristics are verified with the help of the accident statistics method. The proposed ship conflict model can provide technical support for further research in the field of ship traffic conflict, and the experimental analysis results are of great significance for strengthening maritime traffic supervision.

Classification of Tropical Forage Grass Varieties Under Moderate and Severe Water Stress using Naïve Bayes and Kernel Density Estimation

The selection of forage grasses that are more adapted to adverse conditions, such as water scarcity or dry rain periods, is extremely important. Mainly due to the severe climate changes, and the search for more sustainable ways of farming. Forage grasses form the basis of the diet of beef cattle and are also used as a source of biofuels, for erosion control and soil improvement. This work presents a machine learning methodology to obtain classification models for nine forage cultivars, subject to moderate and severe water stress. The Naïve Bayes algorithm is used together with the Kernel Density Estimation method to obtain the densities used in the classification models. Before learning the models, the grouped cross-validation technique and also the grid search are used to search for the best set of hyperparameters. The best accuracy and precision results are 0.88 and 0.90, respectively. It is observed that the classification performance depends on the cultivars used in the training and test sets. At the end, the estimated probability densities are also analyzed by comparing them with some statistics obtained for each variable and water stress or control environments.

Developing a spatio-temporal interactions model for car crashes using a novel data-driven AHP-TOPSIS

We have proposed a data-driven method for spatio-temporal analysis of car crashes based on the Multi Criteria Decision Making (MCDM) procedure in the Zanjan city, NW Iran which are recorded in the period 2019–2020. A combination of Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used in this paper to accurately identify the spatio-temporal interactions exist in car crashes. A data-driven AHP-TOPSIS procedure is arranged based on assigning proper weights to the time series related to the peak of accidents. On the other hand, for spatial analysis, the Kernel Density Estimation method was used to create the continuous-value maps of different traffic accidents and then classified using Natural Breaks Classifier. In fact, the proposed methodology can be used to identify car crash hotspots by considering spatio-temporal interactions as well as addressing exaggerated weightings arising from knowledge-driven modeling. By using the spatio-temporal interaction maps in which the location and time of crashes are considered, simultaneously, it is possible to provide a new scientific strategy for identifying car crash hotspots which can lead to better traffic management, improved allocation of resources, and enhanced prevention regulations.

Exploring visitors’ visual perception along the spatial sequence in temple heritage spaces by quantitative GIS methods: a case study of the Daming Temple, Yangzhou City, China

The Daming Temple, built during 457–464 C.E., is one of the developing ancient temple heritage spaces located in Yangzhou city, P. R. China. Over the past 60 years, variation in visitors’ spatial perception along the tour routes in the temple has occurred. This research attempts to reveal the changes in visitors’ visual perception along the spatial sequences at 3 different times (i.e., 1962, 1973 and 2022). A quantitative GIS-based method, which includes analysing the distribution of visitors’ spatial preferences and spatial configuration, is proposed. Digital landscape tools and quantitative estimation methods are used, including mapping within Rhinoceros software, the kernel density estimation (KDE) method within ArcGIS software and spatial syntax analysis within DepthMap software. Extracted geodata from 500 photographs of the heritage space taken by volunteer visitors are analysed within the GIS environment. Values of the mean depth (MD) at both levels of visibility and accessibility are calculated within the visibility graph analysis (VGA) model. Comparisons between the visual preferences of the visitors and the spatial configuration along the spatial sequence are conducted. The results indicate that the spatial sequence has a significant impact on visitors’ visual preferences and tour routes. The phenomenon of spatial sequence among dynamic temporal variations and the effects of narrative spaces along the spatial sequence are highlighted and explained, which reveal the relationship between visitors’ geospatial preference and the spatial configuration of the temple. Some suggestions are put forwards for further studies on the revitalisation and management of East Asian ancient temple heritage spaces.

Evaluation and Spatiotemporal Differentiation of Cultural Tourism Development Potential: The Case of the Middle and Lower Reaches of the Yellow River

Cultural tourism development potential (CTDP) is the future value and supporting force of the environmental value, economic and social efficiency, innovation ability and supporting system of cultural tourism. At present, there are few relevant studies on CTDP, but the research results on the tourism development potential of cultural heritage are relatively rich, and the existing evaluation methods lack comprehensiveness, dynamics and visualization. Based on systems theory and sustainable development theory, this article attempts to innovate and collect time series data through the entropy method, multi-index comprehensive evaluation method, spatial kernel density estimation method, and centroid transferring model. The temporal and spatial evolution characteristics and the CTDP of 43 cities in the middle and lower reaches of the Yellow River are examined and analyzed. It is found that the CTDP in the middle and lower reaches of the Yellow River is divided into five levels; the overall potential intensity of the research area is small and has significant spatial differences; influenced by the time factor, the interaction and spatial correlation of within the research area are significant; the development of regional cultural tourism has strong regional dependence in the short range. The center of potential gradually moves to the geometric center. This study is significant for promoting the sustainable development of economic tourism in cradles of world civilization.

Spatial Distribution and Influencing Factors of Traditional Villages in Inner Mongolia Autonomous Region

This paper takes 207 traditional villages in Inner Mongolia as the research object and uses the ArcGIS10.7 software platform, using the nearest neighbor index, coefficient of variation analysis, spatial autocorrelation analysis, imbalance index, kernel density estimation method, geographical detector, and other methods to explore the spatial distribution characteristics and influencing factors of traditional villages in Inner Mongolia. The research shows that: (1) the spatial distribution of traditional villages in Inner Mongolia is condensed; the distribution of cities is uneven; and the overall distribution pattern of ‘two main and two vice’ is presented. (2) The traditional villages are mainly distributed in the altitude area of 500–1500 m, and their spatial distribution characteristics are positively correlated with the annual average temperature, annual precipitation, total population, the proportion of the primary industry, and the number of intangible cultural heritage, and negatively correlated with the slope, river distance, highway density, per capita GDP, urbanization, and the proportion of the secondary industry. (3) The results of GeoDetector2018 software show that socio-economic factors are the primary factors affecting the spatial distribution of traditional villages in Inner Mongolia, followed by natural geographical factors. The interaction and synergy between the influencing factors have increased significantly, which jointly affects the spatial pattern of the distribution of traditional villages in Inner Mongolia. The purpose of the study is to provide reference for the protection and development of traditional villages in Inner Mongolia and the implementation of the national rural revitalization strategy.

Robust adaptive analysis of dynamic responses of wave energy converters

PurposeThe purpose of this paper is to exploit a new and robust method to forecast the long-term extreme dynamic responses for wave energy converters (WECs).Design/methodology/approachA new adaptive binned kernel density estimation (KDE) methodology is first proposed in this paper.FindingsBy examining the calculation results the authors has found that in the tail region the proposed new adaptive binned KDE distribution curve becomes very smooth and fits quite well with the histogram of the measured ocean wave dataset at the National Data Buoy Center (NDBC) station 46,059. Carefully studying the calculation results also reveals that the 50-year extreme power-take-off heaving force value forecasted based on the environmental contour derived using the new method is 3572600N, which is much larger than the value 2709100N forecasted via the Rosenblatt-inverse second-order reliability method (ISORM) contour method.Research limitations/implicationsThe proposed method overcomes the disadvantages of all the existing nonparametric and parametric methods for predicting the tail region probability density values of the sea state parameters.Originality/valueIt is concluded that the proposed new adaptive binned KDE method is robust and can forecast well the 50-year extreme dynamic responses for WECs.

Estimation of Continuous Distribution of Iterated Fission Probability Using an Artificial Neural Network with Monte Carlo-Based Training Data

The Monte Carlo neutron transport method is used to accurately estimate various quantities, such as k-eigenvalue and integral neutron flux. However, in the case of estimating a distribution of a desired quantity, the Monte Carlo method does not typically provide continuous distribution. Recently, the functional expansion tally (FET) and kernel density estimation (KDE) methods have been developed to provide a continuous distribution of a Monte Carlo tally. In this paper, we propose a method to estimate a continuous distribution of a quantity in all phase-space variables using a fully connected feedforward artificial neural network (ANN) model with Monte Carlo-based training data. As a proof of concept, a continuous distribution of iterated fission probability (IFP) was estimated by ANN models in two distinct fissile systems. The ANN models were trained on the training data created using the Monte Carlo IFP method. The estimated IFP distributions by the ANN models were compared with the Monte Carlo-based data that include the training data. Additionally, the IFP distributions by the ANN models were also compared with the adjoint angular neutron flux distributions obtained with the deterministic neutron transport code PARTISN. The comparisons showed varying degrees of agreement or discrepancy; however, it was observed that the ANN models learned the general trend of the IFP distributions from the Monte Carlo-based training data.

A fault detection method based on sparse dynamic canonical correlation analysis

AbstractFault detection based on canonical correlation analysis (CCA) has received increased attention due to its efficiency in exploring the relationship between input and output. However, traditional CCA may generate redundant features in both the input and output projections while maximizing the correlations. In this paper, sparse dynamic canonical correlation analysis (SDCCA) is developed for dealing with the fault detection of dynamic processes. Through posing sparsity in the extraction of features, the interpretability of canonical variates is enhanced attributed to the sparsity of canonical weights. Based on the SDCCA model, the monitoring metric is established for fault detection. Moreover, the upper control limit (UCL) based on monitoring metrics is determined by the kernel density estimation (KDE) method to avoid the violation of the Gaussian assumption. The superiority of the proposed SDCCA‐based fault detection method is illustrated through a comparative study of the Tennessee Eastman process benchmark.

Analysis of temporal and spatial evolution characteristics and influencing factors of land use transformation in Hebei Province from the perspective of supply and demand

This study focuses on the counties and districts of Hebei Province as the research unit. It adopts a supply-demand perspective to analyze the spatial-temporal evolution characteristics of farmland utilization transformation in the province, investigate the coordination of this transformation, and explore the influencing factors. The weight of indicators is calculated using the entropy weighting method and Analytic Hierarchy Process The comprehensive evaluation model is then applied to calculate the supply-demand transformation index of farmland utilization in Hebei Province for the years 2005, 2010, 2015, and 2020. Furthermore, the spatial-temporal evolution characteristics are analyzed using the kernel density estimation method. The coupling coordination degree model is selected to explain the relationship between the supply-demand transformation of farmland utilization. Finally, the influencing factors are analyzed using the geographical detector model. The research findings are as follows: 1) The supply-demand transformation index of farmland utilization in Hebei Province has shown an increasing trend during the study period. The standard deviation of the supply transformation index has increased over time, while the demand transformation index has increased at a faster rate. High-density supply transformation is concentrated in the southeast, particularly in the eastern part of Shijiazhuang. Conversely, the northwest exhibits a low-density supply transformation. High-density demand transformation is observed in urban areas across the province, with a significant expansion from 2010 to 2015. 2) There is a strong correlation between the supply and demand transformation of farmland utilization. The coupling coordination degree has gradually improved from 2005 to 2015, transitioning from rapid to stable growth. The level of coupling coordination has shifted from imbalance to coordination. The mountainous areas in the northwest of Hebei Province exhibit relatively lower coupling coordination degrees, while the plains in the southeast demonstrate higher levels. 3) The supply transformation of farmland utilization is closely correlated with the natural environment, particularly elevation and topography. On the other hand, the demand transformation is closely associated with socio-economic development, with a scarcity of supply driving an increase in the demand transformation index. Industrial developed areas show a higher intensity of demand for farmland utilization. 4) To ensure the sustainable utilization of farmland while meeting food production needs, it is crucial to enhance contiguous farmland and mechanization levels, promote the integration of agriculture and tourism, establish an ecological barrier around Beijing-Tianjin, and optimize the ecological compensation mechanism for farmland. The above findings provide valuable insights into farmland utilization transformation and suggest important strategies for its sustainable development.

Analysis of Measurement, Regional Differences, Convergence and Dynamic Evolutionary Trends of the Green Production Level in Chinese Agriculture

The development of green agriculture is conducive to accelerating the agricultural modernization process, making a significance for the sustainable development of agriculture and the environment in China. This paper constructs a comprehensive evaluation model by selecting 17 representative indicators from the perspective of sustainability. Then, this paper uses the entropy value method to measure the level of green agricultural production in 31 provinces from 2011 to 2021. We use Dagum’s Gini coefficient and decomposition method, the kernel density estimation method and spatial Markov chain analysis method to explore the main sources of spatial differences and dynamic evolution trends. The results show that: (1) The overall level of green production in Chinese agriculture is low, and the level in the central region is higher than that in eastern and western regions; (2) There are significant regional differences in the level of green production in China’s agriculture, and this is likely to widen. The interaction of inter- and intra-regional differences is the main reason for overall differences; (3) The trend of polarization in the level of green agricultural production is more obvious in the eastern part of China, while it is weaker in central and western regions; (4) There is a spatial spillover effect in the dynamic evolution of China’s agricultural green production level. And the longer the overall time, the more obvious the spillover effect is. This paper analyzes the past development of green agriculture in China, makes predictions and provides constructive suggestions for the improvement and development of green agricultural production in China in the future.

Analysis of the Temporal and Spatial Pattern and Convergence Characteristics of High-Quality Sustainable Economic Development of Urban Agglomeration

Based on the sample data of 149 cities in ten major urban agglomerations from 2004 to 2019, the entropy method, Dagum Gini coefficient, and three-dimensional kernel density estimation method are used to calculate and describe the spatial pattern of the high-quality, sustainable economic development of these ten major urban agglomerations. We then use the spatial econometric model to estimate the β convergence trend within the urban agglomerations and among the urban agglomerations at different levels. Our main findings include the following: First, the urban agglomeration tends to develop a high-quality economy, but the gap between the urban agglomerations can be large. Second, although the gap within the overall group of urban agglomerations is expanding, the gap between high-quality individuals and the average is constantly shrinking; the gap between groups is still the leading cause of the spatial gap, with a contribution rate of 70.51%. Third, all urban agglomerations have an absolute and conditional β convergence trend, and the convergence speed presents the characteristics of “high level slow, low level fast”. Government intervention, financial development, urbanization, and human capital contribute to the high-quality, sustainable economic development of each urban agglomeration. There is a heterogeneous influence; there is also absolute and conditional β convergence among urban agglomerations at all levels, and the convergence rate presents a gradient characteristic of “third level > second level > first level”, and by balancing the financial relationships between city groups within each level, development differences can promote the dynamic coordination of high-quality, sustainable economic development rates.

Research on the development mode of cultural tourism industry based on the concept of low carbon and environmental protection by big data analysis

Abstract In order to build a low-carbon and environmentally friendly cultural tourism business, this research analyzes data on user characteristics, tourist attractions, and functional qualities. The clustering technique is used to examine the clustering features of the industry development, and the spatial variation characteristics of the industry development are developed with the aid of the closest neighbor index, standard ellipse, and kernel density estimation methods. In order to analyze the degree of development of this industry, the integration degree model was constructed by the entropy weighting method. The analysis shows that the growth rate of the central and western regions is 32.2% and 28.8%, respectively, while the growth rate of the eastern region is 22.7%. The integration degree of the eastern region ranges from 0.12 to 0.85, the integration degree of the western region ranges from 0.18 to 0.9, and the integration degree of the central region ranges from 0.2 to 0.8. Based on this study, the integration development of the low-carbon environmental protection concept and the cultural tourism industry is thriving.

Consistent or inconsistent? The club convergence effect and matching degree analysis on energy consumption intensity and production density: Evidence from the data of 2000–2020 in China

Clarifying the regional differences in coal production and consumption is important for the adjustment of the energy structure. To understand the spatial and temporal variability of coal production and consumption, this study examined coal consumption and production across 30 Chinese provinces over the period of 2000–2020 using the methodology introduced by Phillips and Sul. Then, we used the kernel density estimation method to analyze the dynamic changes. Finally, the Gini coefficient and coefficient of variation methods were used to quantify the unevenness in the production and consumption of coal. The analytical results strongly support the existence of convergence clubs. We find five convergent clubs for coal consumption and six convergent clubs for coal production. There are significant differences in the geographic distribution of convergence clubs for coal consumption and coal production. In addition, the difference in coal consumption levels between provinces widened, while the difference in coal production narrowed during the study period. The unevenness of the regional distribution and matching of coal production and consumption increased. Overall, the regional unevenness of coal production and consumption should be given sufficient attention. This study provides a reference on how to differentiate and adjust the production and consumption of coal in each region.

Analysis of the coupled and coordinated development of sports and tourism industries and the driving factors

The Yellow River Basin has a wealth of tourism resources, a long history of folk sports, and strong legislative support, all of which are advantages for the growth of the sports tourism sector. This study constructs an evaluation index system based on panel data from nine provinces and regions in the Yellow River Basin from 2011 to 2020, and then measures the development index and analyzes the driving factors using the entropy method, coupling coordination degree model, kernel density estimation method, and grey correlation analysis method. The findings demonstrate that (1) the Yellow River Basin's overall level of growth in the sports and tourist sectors exhibits a consistent and upward trend and that Shandong and Henan provinces clearly have an edge over other provinces in terms of development. (2) The geographical development pattern was lower Yellow River > upper Yellow River > middle Yellow River, and the coupling coordination status shifted from slight disorder to good coordination. (3) There is a close connection between sports tourism and variables including population density, financial prowess, and infrastructure growth. The employment population index has the smallest link with invention patent authorization. The macro-policy framework should therefore be strengthened in the future, the sports tourism infrastructure should be improved, and the Yellow River basin's unique sports tourism resources should be fully utilized in order to increase brand influence. In addition, new sports tourism products should be developed in response to market demand and consumer preferences, and enterprise innovation and research and development efforts should be increased in order to achieve high-quality development transformation.

Probabilistic short-term wind speed forecasting using a novel ensemble QRNN

Neural Networks are widely used as the intelligent tool for short-term wind speed forecasting. Owing to the inherent randomness and intermittency of the wind, it is hard to ensure sufficient forecasting precision when using single conventional Network models to conduct deterministic forecasting. Thanks to the recent achievements of ensemble methods, a novel ensemble Quantile Regression Neural Network (QRNN) based on Wavelet decomposition (WD) and least absolute shrinkage and selection operator (LASSO) for probabilistic short-term wind speed forecasting is developed here. The signal processing technology, feature extraction method and predicting model are ingeniously combined in the proposed ensemble QRNN. More specifically, the WD is utilized to extract the signal features into a collection of easy-to-analyze subseries. LASSO regression is combined with the QRNN to conduct the robust prediction. Finally, the kernel density estimation method is adopted to further enhance the forecasting precision. The forecasting performance between the proposed ensemble QRNN and several benchmark models are evaluated using a piece of field measured 10-min mean wind speed data. Results show that the proposed ensemble model can adequately enhance the performance of short-term wind speed forecasting, which can also quantify and reduce the uncertainty of the prediction results.

Integrated Multi-Head Self-Attention Transformer model for electricity demand prediction incorporating local climate variables

This paper develops a trustworthy deep learning model that considers electricity demand (G) and local climate conditions. The model utilises Multi-Head Self-Attention Transformer (TNET) to capture critical information from G, to attain reliable predictions with local climate (rainfall, radiation, humidity, evaporation, and maximum and minimum temperatures) data from Energex substations in Queensland, Australia. The TNET model is then evaluated with deep learning models (Long-Short Term Memory LSTM, Bidirectional LSTM BILSTM, Gated Recurrent Unit GRU, Convolutional Neural Networks CNN, and Deep Neural Network DNN) based on robust model assessment metrics. The Kernel Density Estimation method is used to generate the prediction interval (PI) of electricity demand forecasts and derive probability metrics and results to show the developed TNET model is accurate for all the substations. The study concludes that the proposed TNET model is a reliable electricity demand predictive tool that has high accuracy and low predictive errors and could be employed as a stratagem by demand modellers and energy policy-makers who wish to incorporate climatic factors into electricity demand patterns and develop national energy market insights and analysis systems.