Technical Indicators Research Articles

Carbon emission efficiency of land use in urban agglomerations of Yangtze River Economic Belt, China: Based on three-stage SBM-DEA model

Exploring regional differences in land use carbon emission efficiency (LUCE) and the path of collaborative emission reduction in the Yangtze River Economic Belt (YREB) are of immense importance to achieve the sustainable regional development. Firstly, based on the estimation method of land use carbon emissions, this paper scientifically calculates the carbon emission of land use of the Yangtze River Delta urban agglomeration (YRD), the middle reaches of the Yangtze River urban agglomeration (MRYR), and the Chengdu-Chongqing urban agglomeration (CC) from 2010 to 2021 in the YREB. And the carbon emission of land use of each city in urban agglomerations was obtained, which was regarded as the unexpected output in the efficiency evaluation index system. Secondly, the three-stage SBM-DEA model was used to reveal the LUCE and its spatiotemporal characteristics in different regions from a static perspective. The empirical results of the second stage SFA showed that the level of economic development, urbanization rate, industrial structure, non-agricultural conversion rate of agricultural land, the level of scientific and technological innovation and government intervention had significant effects on LUCE. By comparing the efficiency values and their decompositions in the first and third stage of various urban agglomerations, it was found that after eliminating environmental and random factors, the average ranking of comprehensive technical efficiency (TE) of land use carbon emission changed from YRD > MRYR > CC to YRD > CC > MRYR due to the cross influence of pure technical efficiency (PTE) and scale efficiency (SE). Thirdly, Malmquist index model was used to analyze the change of total factor productivity index (TFPch) and its spatiotemporal pattern from a dynamic perspective. It was found that the TFPch, technological progress index (TC), pure technical efficiency index (PEC) and scale efficiency index (SEC) of land use carbon emission of the three major urban agglomerations all showed varying degrees of increase after adjustment. Finally, to further analyze the impact of technological progress, PTE and SE on LUCE, a regression model was constructed with LUCE of each city in urban agglomeration as the dependent variable, and TC, PEC and SEC as independent variables. It was concluded that technological progress was the main factor to promote the improvement of LUCE in the whole YREB. The trend of changes of LUCE in various urban agglomerations was closely related to its management level, technology level and factor structure adjustment. The above conclusions provide support for the coordinated emission reduction path of urban agglomerations in the YREB from the perspective of land use, which is conducive to actively and steadily promoting the realization of carbon peak and carbon neutrality goals.

Optimization of short-term stock selection based on volume and price using a non-cooperative parallel DEA model

This paper presents a novel approach to portfolio optimization in the field of finance, with a specific focus on short-term yield. Existing literature has mainly utilized fundamental data to predict long-term trends in stock prices, but our proposed methodology utilizes technical indicators based on the theory of chasing up. Furthermore, we address the non-cooperative nature of volume and price fluctuation indicators and introduce non-cooperative theory into the short-term volume and price stock selection scheme for the first time. We propose an optimization of short-term stock selection based on volume and price using a non-cooperative parallel Data Envelopment Analysis (DEA) model, which we apply to Chinese main board listed companies. Our empirical results demonstrate the effectiveness of our model in selecting high-yield stocks in the short term. This paper contributes to the ongoing discussion on portfolio optimization and presents a compelling solution for investors seeking to maximize their financial gains. The proposed methodology can be utilized in practical applications and has significant implications for the financial industry.

Apply an Inclined Track to Enhance the Performance of Fosbury Flop High Jump Athletes

Background and Aim: The high jump was a dominant sport in China, with three athletes breaking the world record five times. However, in the following period, the high jump performance of Chinese athletes showed a declining trend, and the gap with the world’s best high jump athletes was obvious. Many studies have shown that speed ability is the main influence factor of the high jump, to change the current situation of the Chinese Fosbury flop high jump sport, this study designed an inclined track to improve the speed ability of athletes, to achieve the purpose of improving performance. This paper aims to apply the inclined track to improve Fosbury flop high jumpers’ speed ability. Materials and Methods: 10 experts were selected for this study and interviewed to determine the test metrics. An inclined track was designed and made, and 5 experts conducted IOC tests on the inclined track. In addition, 5 Fosbury flop high jumpers from Xi'an Physical Education University were selected for the study to experiment. t-test independent was performed on the technical index data using a computer software package. Results: The research results show that: (1) Inclined track had a high validity (IOC=.97) and safety for training. And (2) Comparison within 5 high jumpers found that all of the variables (Velocity of center of gravity, Step lengths for the last 2 steps, Step lengths for the last 1 step) were significant (*P<.05). Conclusion: The research suggests that the inclined track is a valid and safe training method. Additionally, the study found significant differences in key variables among high jumpers, emphasizing the importance of these factors in high jump performance.

Cluster Based Classification Model for Trend Prediction in Time Series Data

Abstract: Real-time stock market trend prediction plays a essential role in the technical analysis for trend prediction process. Traditional historical technical indicators are difficult to trace and predict the trend due to noise or uncertainty in the training dataset. Since, most of the traditional homogeneous or heterogeneous stocks are used to predict the overall market sentiment for the institutions or retailors. Also, traditional approaches are difficult to find the outliers in the technical indicators data for the clustering process. These models only consider the static metrics or static outlier meaures due to variation in data distribution according to time basis. In this work, a hybrid knn classifier is used to predict the trend of the stock.

Development and Application of Technical Key Performance Indicators (KPIs) for Smart Water Cities (SWCs) Global Standards and Certification Schemes

Smart water cities (SWCs) use advanced technologies for efficient management and preservation of the urban water cycle, strengthening sustainability and improving the quality of life of the residents. This research aims to develop measurement and evaluation tools for SWC key performance indicators (KPIs), focusing on innovative water technologies in establishing unified global standards and certification schemes. The KPIs are categorized based on the stage at which water is being measured, namely the urban water cycle, water disaster management and water supply and treatment. The objective is to assess cities’ use of technologies in providing sufficient water supply, monitoring water quality, strengthening disaster resilience and maintaining and preserving the urban water ecosystem. The assessment is composed of a variety of procedures performed in a quantitative and qualitative manner, the details of which are presented in this study. The developed SWC KPI measurements are used to evaluate the urban water management practices for Busan Eco Delta City, located in Busan, South Korea. Evaluation processes were presented and established, serving as the guideline basis for certification in analyzing future cities, providing integrated and comprehensive information on the status of their urban water system, gathering new techniques, and proposing solutions for smarter measures.

Development of a system for controlling a diamond drilling mode in terms of rock fault resistance

Relevance. Resistance to rock-cutting elements in rock destruction largely determines the effectiveness of drilling results. Information about the drag coefficient allows you to correctly and timely adjust the intensity of a drilling tool impact on the bottom of the well. To control the force contact of the drilling tool cutters with the bottom of the well, it is necessary to have a methodological apparatus that allows determining the rock destruction mechanism, taking into account the resistance forces. The existing methods for selecting combinations of diamond drilling mode parameters according to the known RPI index or according to the criterion of energy intensity of rock destruction N/vm , where N is the bottomhole power to overcome the resistance during rock destruction at the bottom hole; vm – mechanical drilling speed, have a number of disadvantages. They consist, for example, in occurrence of tool wear or the need to obtain a bottomhole power value in the current time mode, which is difficult under modern drilling conditions. Therefore, the study of rock resistance to destruction by a diamond cutter, based on application of the full factorial experiment method with obtaining mathematical models of factors and their graphical interpretation in combination with process control systems using computer technology in the current time mode, is a relevant topic. The disclosure of this topic will determine the direction of increasing technical and economic indicators of drilling. Aim. To develop a methodological apparatus that allows determining the mechanism of rock destruction taking into account the resistance forces to ensure optimal control of diamond drilling. Objects. Mechanism of rock resistance to destruction by a diamond cutter. Methods. Analysis, analytical research, full factorial experiment. Results. The paper presents an analytical analysis of the possibility of controlling drilling modes in order to achieve the greatest effect from drilling by estimating the drag coefficient as a function of the intensity of destruction or deepening per one revolution. Based on the analytical studies and the mode of drilling control by a computer in the real time mode, the authors have proposed a method for selecting the parameters of the drilling mode according to a given optimal value of the resistance index during drilling, and a way for its automatic implementation.

OPTIMIZATION OF SHOPPING STRATEGY IN CRYPTOCURRENCY MARKETS BASED ON ARTIFICIAL NEURAL NETWORKS

This study is relevant in the context of modern finance, where neural networks play a key role in the analysis and forecasting of price dynamics. The research focuses on identifying buy signals for the BTCUSDT and ETHUSDT trading pairs in the cryptocurrency market. To construct a neural network model that can automate the identification of moments beneficial for purchasing selected assets, various technical analysis indicators and convolutional neural networks (CNN) were used. The research includes an analysis of scientific literature, data collection, indicator selection, signal algorithm development, and the construction of neural network models. The main contribution of this work lies in the development and testing of models capable of predicting buy signals with a high accuracy, confirmed by accuracy indicators of over 92%. The findings of this study can be useful for private investors and financial institutions in forming investment strategies based on machine learning.

Optimization of Traditional Stock Market Strategies Using the LSTM Hybrid Approach

Investment decision-makers increasingly rely on modern digital technologies to enhance their strategies in today’s rapidly changing and complex market environment. This paper examines the impact of incorporating Long Short-term Memory (LSTM) models into traditional trading strategies. The core investigation revolves around whether strategies enhanced with LSTM technology perform better than traditional methods alone. Traditional trading strategies typically depend on analyzing current closing prices and various technical indicators to take trading action. However, by applying LSTM models, this study aims to forecast closing prices with greater accuracy, thereby improving trading performance. Our findings indicate that trading strategies that utilize LSTM models outperform traditional strategies. This improvement suggests a significant advantage in using LSTM models for market prediction and trading decision making. Acknowledging that no one-size-fits-all strategy works for every market condition or stock is crucial. As such, traders are encouraged to select and tailor their strategies based on thorough testing and analysis to best suit their needs and market conditions. This study contributes to a better understanding of how integrating LSTM models can enhance traditional trading strategies, offering a path toward more effective decision making in the unpredictable stock market.

Farm strategies and characteristics influencing profitability in Nigerian catfish aquaculture: Lessons on resilience during economic crisis and COVID‐type shock

AbstractSince the early 2000s, aquaculture in Nigeria has grown exponentially. African catfish aquaculture was a major contributor to this expansion. However, a fall in output started in 2015, mostly due to challenging economic conditions which affected farmer's profitability. This challenge has been exacerbated by the COVID‐19 pandemic. Notwithstanding, some catfish farms remained profitable. This study investigates the profitability strategies that aid farmers' resilience in times of both economic distress and COVID‐type shock. Data from 609 randomly sampled farms across the country were verified and included in the study covering 1118 operations for the pre‐COVID and COVID periods. Budgetary analysis was done, and stepwise regression was used to identify factors that most contribute to profitability. These factors were ranked by average profit per kilogram and grouped. The profitability during the periods was influenced by factors within the subcategories of scale of operation, experience, targeted markets, ownership, and pond type. Engagement in other agricultural ventures favored profitability during the COVID period. Profitable and non‐profitable groups were further characterized based on their technical and financial indicators. Explored strategies for profitability, including the operation scale, marketing tactics, cost‐cutting, and innovative production techniques all proved successful during economic distress and the pandemic.

Evaluation method of modification effect of direct-to-plant SBS modifier on asphalt

To accurately evaluate the modification effect of direct-to-plant styrene–butadiene–styrene block copolymer (SBS) modifier on asphalt, a collection method for direct-to-plant SBS-modified asphalt was proposed in this paper, the qualitative and quantitative evaluation index system was established, and the feasibility of quantitative evaluation based on fluorescence microscopy images and visual inspection of the mixing effect was evaluated, the feasibility of quantitative evaluation indicators was also verified based on the asphalt indicators, rheological properties, and asphalt mixtures's pavement performances. The research results indicate that based on fluorescence microscopy images, by observing the distribution and swelling state of direct-to-plant SBS modifiers can qualitatively evaluate the modification effect of direct-to-plant SBS modifiers on asphalt. Based on the visual inspection of the mixing effect, observe the size and quantity of unmelted particles, determine the melting state of the direct-to-plant SBS modifier, and its modification effect on asphalt can be qualitatively evaluated. For the same direct-to-plant SBS-modified asphalt, the percentage of rotation viscosity between direct-to-plant SBS-modified asphalt and traditional SBS-modified asphalt at 135 ℃ (DAη(135℃)) and discrete coefficient of 135 ℃ rotational viscosity of direct-to-plant SBS-modified asphalt (CVη(135℃)) exhibit a linear relationship with direct-to-plant SBS-modified asphalt's penetration, softening point, and ductility, the smaller the CVη(135℃), the greater the DAη(135℃). The smaller the CVη(135℃) (the larger the DAη(135℃)), the larger the dynamic shear modulus (G*), the smaller the phase angle (δ), and the larger the rutting factor (G*/sinδ) at the same scanning temperature. The relationship between CVη(135℃) (DAη(135℃)) and direct-to-plant SBS modifier on the main technical indicators and rheological performance indicators of asphalt is relatively stable. There is a clear relationship between CVη(135℃) (DAη(135℃)) and the high-temperature stability, low-temperature crack resistance, and water stability of direct-to-plant SBS-modified asphalt mixture, all of which show that as CVη(135℃) decreases (DAη(135℃) increases), the pavement performances improve.

Design of Parabolic Off-Axis Reflector Optical System for Large Aperture Single Star Simulators

This study proposes a parabolic off-axis reflective optical system design method to reduce the wave aberration of the optical system of a large aperture single star simulator and improve the optical system’s imaging quality. Firstly, we determined the design indexes of the optical system of the large aperture single star simulator by analyzing the technical indexes of the star sensitizer and the development status of the single star simulator; secondly, the initial structural parameters of the optical system were calculated based on the theory of primary aberration; then, we carried out the design optimization of the optical system, the image quality evaluation, and the tolerance analysis using Zemax software; finally, the study tested the wave aberration of the optical system by using the four-dimensional interferometer and the standard mirror together. The simulation results of the optical system are as follows: in the entire field of view, the aberration of the optical system is far less than 0.002%, the modulation transfer function (MTF) reaches the diffraction limit, and the maximum wave aberration is 0.0324 λ. The experimental results are as follows: the maximum wave aberration of the optical system is 0.0337 λ, which is less than 1/25 λ, and it meets the requirements of the index. The simulation and experimental results show that the optical system of the large aperture single star simulator designed by this method has good imaging quality and a simple system structure.

Technical indicator enhanced ultra‐short‐term wind power forecasting based on long short‐term memory network combined XGBoost algorithm

AbstractThe growing integration of renewable energy sources into the power grid has introduced unprecedented uncertainty. Ensuring an appropriately scheduled reserve is essential to accommodate renewable energy's intermittent and volatile nature. This study introduces an innovative approach to ultra‐short‐term wind power forecasting, which relies on feature engineering and a hybrid model. The effectiveness of this proposed method is showcased through a case study involving a utility‐scale wind farm in Inner Mongolia, China. The findings indicate that the hybrid model, which combines the XGBoost (Extreme Gradient Boosting) algorithm and LSTM (Long Short‐Term Memory) network with KDJ (Stochastic Oscillator), and MACD (Moving Average Convergence and Divergence), achieves the highest forecasting accuracy. Specifically, the proposed model yields a normalized mean absolute error of 0.0396 for wind power forecasting. The modelling and forecasting process takes approximately 550 s. Furthermore, the suggested method is employed to predict wind power and wind speed for a wind farm in the USA. The experimental results consistently indicate that the proposed model maintains a dependable performance across various raw datasets; it is suitable for use in power system operations.

Effect of COVID-19 on Key Performance Indicators of Spanish Professional Soccer League.

The unprecedented COVID-19 health crisis severely disrupted global sports in 2020, prompting lengthy suspensions followed by resumed competitions under abnormal behind-closed-doors conditions without fans. These disruptions necessitated tactical adaptations by coaches and teams, attempting to still achieve successful outcomes. This study investigates the pandemic's impacts on performance metrics and indicators within Spanish professional soccer. Utilizing systematic notational analysis, 760 match cases from the 2019-2020 La Liga season were examined, comprising 27 matchdays from the pre-COVID context and 11 after resumption. Multivariate tests identified significant pre/post differences and interactions for various technical indicators including shots, cards, corners, and offside calls. The pandemic was associated with a reduction from 12 to just 5 identifiable playing styles, suggestive of increased conservatism featuring more passive play, limited attacking depth, and horizontal ball movement. Such tactical changes appear provoked by condensed fixture scheduling post-lockdown, the lack of supportive crowds, and compromised player fitness/recovery. By quantifying these COVID-precipitated changes, the analysis provides tangible evidence for coaches to make informed adjustments in training and preparation for functioning effectively in disrupted environments. The findings emphasize that versatility and flexibility will be vital to optimize performance during times of unprecedented uncertainty.

Research on quantitative evaluation of revise requirements of Specifications of Air Sampling for Hazardous Substances Monitoring in the Workplace (GBZ 159-2004)

Objective: To conduct quantitative evaluation on the revise requirements of Specifications of Air Sampling for Hazardous Substances Monitoring in the Workplace (GBZ 159-2004) , clarify the problems and suggestions during its implementation for improvement, and provide a basis for the revision of the standard. Methods: From April to September 2021, stratified convenient sampling method was adopted and semi-open questionnaire was used to investigate the occupational health personnel in CDC, occupational prevention and control institutes, employers, third-party technical service institutions and universitie. The entropy weight of each index and the score based on entropy weight of GBZ 159 were calculated. Spearman rank correlation analysis was used to describe the correlation between the two indexes and radar chart was drawn for comprehensive evaluation. Results: A total of 151 questionnaires were received from the respondents, of which 147 were valid, with an effective recovery rate of 97.35%, involving 29 provinces, autonomous regions and municipalities. The median G scores of the necessity and urgency of GBZ 159 revision based on entropy weight were 2.84 and 3.17, respectively, and the difference was statistically significant (M=-25.50, P<0.001) . The trend of the score G of necessity and urgency based on entropy weight was basically the same for all secondary items (r(s)=0.9998, P<0.001) , and the score G of urgency based on entropy weight was higher than that of necessity. The highest score G of necessity and urgency based on entropy weight was "3.13 long time sampling", which were 7.56 and 8.23 respectively. This was followed by "3.12 short time sampling", which were 7.19 and 7.13 respectively. Conclusion: GBZ 159 has encountered some new problems and challenges in the implementation process, and some of its technical indicators have been out of line with the actual practice of occupational health at present. These are the two items that urgently needs to be revised and improved, such as "3.13 long time sampling" and "3.12 short time sampling" and other items need to be revised and improved.

Study on the implementation effect evaluation of Specifications of Air Sampling for Hazardous Substances Monitoring in the Workplace (GBZ 159-2004)

Systematically evaluate the implementation of Specifications of Air Sampling for Hazardous Substances Monitoring (GBZ 159-2004) , so as to provide technical basis for the future standard revision. The semi-structured interview method was used to interview the industry experts from centers for disease control and prevention, occupational disease prevention and control hospitals/institutes, employers, third-party technical service institutions and universities, and the induction method was used to refine the topics and relevant suggestions. Some technical indicators of GBZ 159 are not suitable for the current actual work of occupational health, and need to be revised and improved urgently. The revised GBZ 159 should comply with the current situation of China's industrial development and the development of occupational health testing equipment in the new era, and improve the relevant technical requirements of sampling quality control.

Life Cycle Assessment of Energy Storage Technologies for New Power Systems under Dual‐Carbon Target: A Review

Aiming at the grid security problem such as grid frequency, voltage, and power quality fluctuation caused by the large‐scale grid‐connected intermittent new energy, this article investigates the life cycle assessment of energy storage technologies based on the technical characteristics and performance indicators. First, the new power system under dual‐carbon target is reviewed, which is compared with the traditional power system from the generation side, grid side, and user side. Based on the power characteristics of the new power system, the energy storage mechanism and energy storage characteristics of mechanical energy storage, electrochemical energy storage, chemical energy storage, electromagnetic energy storage, and thermal energy storage are described. Then, compared with the existing research strategies, a comprehensive life cycle assessment of energy storage technologies is carried out from four dimensions: technical performance, economic cost, safety assessment, and environmental impact. Moreover, the suitable scenarios and application functions of various energy storage technologies on the power generation side, grid side, and user side are compared and analyzed from the working principle, key technologies, advantages and disadvantages, and technical difficulties of energy storage technologies. Finally, combined with the energy storage technologies in the electricity market, four profit models are analyzed.

Forecasting the high‐frequency volatility based on the LSTM‐HIT model

AbstractVolatility forecasting from high‐frequency data plays a crucial role in many financial fields, such as risk management, option pricing, and portfolio management. Many existing statistical models could better describe and forecast the characteristics of volatility, whereas they do not simultaneously account for the long‐term memory of volatility, the nonlinear characteristics of high‐frequency data, and technical index information during the modeling phase. The purpose of this paper is to use the prediction advantage of deep learning long short‐term memory (LSTM) model to predict the volatility fusing three classes of information, that is, high frequency realized volatility (H), technical indicators (I), and the parameters of generalized autoregression conditional heteroskedasticity(GARCH), heterogeneous autoregressive (HAR), and c, resulting in a novel LSTM‐HIT model to forecast realized volatility. We employ the extreme value theory (EVT) of a semiparametric method to estimate the quantile of standardized return and construct the LSTM‐HIT‐EVT model to forecast the value at risk (VaR). Empirical results show that the LSTM‐HIT model provides the most accurate volatility forecast among the various considered models and that the LSTM‐HIT‐EVT model yields forecasts more accurate than other VaR models.

Performance Study of Black Shale Modified Soil for Road Use Based on Eshelby–Mori–Tanaka Theory

Black shale, as a type of soft rock, exhibits high strength when freshly exposed. However, it easily disintegrates upon contact with water, making it unsuitable for direct use in roadbed construction. Using it as discarded material not only increases construction costs but also pollutes the environment. Therefore, the reuse and modification of black shale have become particularly important. Based on the theory of composite material equivalent inclusions, this study investigates the strength and water stability characteristics of black shale gravel after being mixed with cement and compacted with clay. The results show that the strength of cemented soil increases linearly with the cement content. The water absorption properties of the modified soils with different amounts of black shale added are similar, with an average water absorption rate of about 2.53%. The strength of black shale modified soil is generally positively correlated with the cement content, although the linear correlation is not significant. The modified black shale soil used in the experiment is suitable for the subgrade of medium- and light-grade secondary roads and below. The recommended mass ratio is Mshale:Mclay:Mcement = 70:21:9. The unconfined compressive strength of the material under 7-day curing is 1.36 MPa. The relationship between the strength of modified soil, clay strength, cement content, and gravel addition has been established, clarifying the physical significance of each parameter. The “drying and soaking” cycle can accelerate the strength degradation of modified soil. It is recommended to strengthen the construction of roadbed drainage facilities during construction to maintain a stable and dry environment for the modified soil as a roadbed filling material. The research results not only provide clear technical indicators for the reuse of discarded black shale in engineering but also serve as a basis for proportion of crushed stone discarded material as roadbed fill.

Comprehensive Assessment and Empirical Research on Green and Low-Carbon Technologies in the Steel Industry

The iron and steel industry is the leading industry supporting China’s industrial sector. Currently, there is less assessment work on green and low-carbon technologies for the iron and steel industry. This study clarifies the overall strategy of technology assessment by researching the relevant theories and methods of technology assessment. The study further establishes a scientific and reasonable comprehensive assessment index system of green and low-carbon technologies for the iron and steel industry from the aspects of technology index, economy and promotion, and application, including factors such as 11 indexes, the amount of energy saving, carbon dioxide emission reduction, and the resource recovery rate by utilising analytical and comprehensive methods and combining with the characteristics of the technologies. By analysing and comparing the advantages and disadvantages of the commonly used assessment methods, the entropy weighting method, grey correlation analysis method, and TOPSIS (technique for order preference by similarity to an ideal solution) method are combined and optimised to construct a comprehensive assessment model. The Latin hypercube sampling method is also introduced to analyse the technical parameters in combination with the evaluation model. Finally, fourteen iron and steel green and low-carbon technologies were selected for case assessment and uncertainty analysis of technical parameters, and it was found that the comprehensive assessment result of gas combined cycle power generation technology was optimal. After determining the weights of each assessment indicator through the entropy weighting method, it is concluded that the technical performance indicator &gt; economic indicator &gt; promotional indicator. A comparative analysis of the results under the three preference decisions concludes that technical performance is the main obstacle to improving the comprehensive assessment score of the technology, followed by the economics of the technology. Finally, the uncertainty analysis of the technical parameters shows that the fluctuation of the technical parameters not only affects the performance of the technology, but also affects the weights of the indicators and the comprehensive evaluation results of the technology.

A techno-economic investigation for utilizing solar energy in irrigation of palm trees in Saudi Arabia

This paper presents a techno-economic investigation for utilizing photovoltaic solar energy in water pumping applications for the irrigation of Palm trees in the Qassim region in Saudi Arabia. The Analysis has been done by applying four technical indicators and three economic indicators on a real farm of palm trees. The investigation took into account the varied water demand for palm trees over the years, meteorological data of the region, the characteristics of the borehole, and the local market prices of PV system components. The investigation has been done using two options of PV systems: grid-connected system and standalone system. The results showed the superiority of the grid-connected system in spite of the unfair price of energy exchange with the utility grid. The results achieved are the Levelled cost of energy, which is in the range from 0.013 to 0.019 $/kWh. The standardized cost of produced water is in the range from 0.011 to 0.013 $/m3, and the simple payback time is in the range from 9.65 to 12.22 years. The results are considered to encourage farmers in the region to convert to solar energy utilization.