Varying Load Demand Research Articles

Review: Relay coordination in DGs with Electric Vehicle

This research offers a comprehensive examination of the most effective approach to synchronize overcurrent relays within a protective system alongside protective relay. Every effort has been made to encompass all potential strategies for achieving optimal coordination among overcurrent relays. Both modern approaches, like Harris hawk optimisation, and more traditional ones, such ground-based circuit breakers, are included in this category of overcurrent protection with Electric Vehicle. The operation of a Smart Grid [1] is characterized by varying load demands, generation levels from DGs, and charging/discharging behaviours of EVs. These factors can lead to different fault currents and altered fault impedance profiles at various times. An adaptive coordination scheme can dynamically adjust relay settings based on these changing conditions to optimize sensitivity and minimize coordination time. In this paper briefly discuss traditional approaches but focus mostly on using Harris Hawk Optimisation to enhance the coordination of overcurrent relays. This document compiles citations for all relevant works and offers a concise overview of the study conducted. Furthermore, the outcomes of these methods are documented in their corresponding references.

Optimization of wind-solar hybrid microgrids using swarm intelligence algorithms

The study explores the enhancement of wind-solar hybrid microgrids via the use of Swarm Intelligence Algorithms (SIAs). It assesses the efficacy of these algorithms in efficiently managing renewable energy sources, load demands, and battery storage inside the microgrid system. An examination of actual data highlights the influence of environmental elements on the production of electricity, as seen by the diverse wind speeds resulting in power outputs that range from 15 kW at 4 m/s to 30 kW at 7 m/s. This underscores the clear and direct relationship between wind speed and the amount of power created. Likewise, solar irradiance levels demonstrate oscillations ranging from 500 W/m² to 800 W/m², therefore yielding power outputs that include a range of 15 kW to 24 kW, so illuminating the profound impact of solar irradiance on energy capture. The dynamic energy consumption patterns are exposed by the varying load demands, whereby the demand levels oscillate between 20 kW and 28 kW. This highlights the crucial significance of demand variability in determining energy needs. In addition, the data on battery storage reveals a range of charge levels, ranging from 25 kWh to 40 kWh, which underscores its pivotal function in the equilibrium of energy supply and consumption. When evaluating SIAs, it becomes evident that Particle Swarm Optimization (PSO) surpasses both Ant Colony Optimization (ACO) and Genetic Algorithms (GA) in obtaining an impressive 80% renewable energy penetration rate. PSO effectively reduces operating costs by 15%, demonstrating its exceptional proficiency in optimizing microgrid operations. This study provides valuable insights into the intricate interplay among environmental conditions, load demands, battery storage, and algorithmic optimization in wind-solar hybrid microgrids.

Intelligent Renewable Energy Agent‐Based Distributed Control Design for Frequency Regulation and Economic Dispatch

The Distributed Renewable Energy Sources (DRESs) integrate hybrid microgrid and prosumer activities that constitute a dynamic system characterized by unknown network parameters. The dynamic system faces challenges, such as intermittent power supply due to low inertia, renewable intermittence, plug‐and‐play prosumer activities, network topology variations, and a lack of constraint handling. These complexities pose significant issues in designing effective control for frequency regulation and consensus‐based economic load dispatch (ELD) within DRES to meet varying load demands. To address the above challenges, this research employs a machine learning‐based distributed multiagent consensus design that offers a rapid and robust approach, mitigating the limitations associated with the Distributed Average Integral (DAI) control design. The proposed multiagent scheme empowers the successful implementation of ELD and frequency regulation, accommodating the intermittent DRES, diverse network topologies, and the dynamic plug‐and‐play activities of prosumers. Moreover, an optimization‐based DAI tuning model is introduced to overcome tuning limitations. Intelligent renewable energy agents are trained through machine learning‐based regression models that use root mean square error metrics for performance evaluations. The intelligent agents employ DAI control to overcome inherent limitations. The effectiveness of the machine learning‐based DAI is thoroughly evaluated using the DRES‐based IEEE 14‐bus hybrid microgrid system. The quantitative results prove its efficacy in addressing the complex challenges of integrated microgrid dynamics.

Optimal design of multiuse hybrid microgrids power by green hydrogen–ammonia

The design and operation of hybrid microgrids (MGs) has attracted much interest. The creation of adaptable standalone hybrid systems that can satisfy interconnected clients’ energy needs using coupled green hydrogen–ammonia has been studied less. Additionally, the capability of scaling up hydrogen generation and fuel decarbonization is little studied. This study proposes an optimized design approach for multiuse hybrid MG supplying power gas, heat, and oxygen with green hydrogen and ammonia carriers. The suggested one-layer technique is implemented using PSO and performs both the optimal size and operation of the proposed system based on hourly data intervals to better track load variations. The proposed technique aims to minimize costs besides ensuring maximum reliability in light of load demand and RERs variations. CHMS is implemented to constantly fulfill heat loads by dispatching power and heat of ICE, FC and EBs. Decarbonization of natural gas is suggested by hybrid hydrogen–ammonia rather than hydrogen-based methanation only. The Proposed methodology is tested on a number of scenarios with various setups. The outcomes are evaluated against existing literature from both the same and different research regions. The best LCOE is 0.0519, 0.04122, and 0.05383 $/kWh, with nearly no energy supply loss and no emissions, according to the results of the vital scenarios SC6, SC8, and SC9. The obtained results reveal the following: (1) When CHMS is utilized, a significant reduction in investment cost results. (2) A single-layer optimization strategy can effectively locate the optimal solution with minimum ratios of curtailed energy. (3) The oxygen produced from hydrogen and ammonia-related conversions is essential in reducing total costs.

Bio-Inspired Strategies for Minimizing Distortion in DC-AC Converters

The global shift towards sustainable energy sources, such as solar and wind power, underscores the critical role of power converters in renewable energy systems. These converters are essential in the process of converting direct current (DC) generated from renewable sources into alternating current (AC), suitable for use in homes and distribution through the grid. The efficiency and performance of these converters have far-reaching implications for the effective integration of renewable energy into the existing grid infrastructure. However, conventional control methods often fall short in optimizing DC-AC converters to adapt dynamically to changing environmental conditions and varying load demands. This letter aims to provide a holistic view of research on bio-inspired optimization strategies and their impact on minimizing distortion, maximizing efficiency, and enhancing the overall reliability of DC-AC converters within renewable energy systems

Design and Analysis of Variable-Stiffness Fast-Steering Mirror

The non-variable stiffness of the flexible hinge in the fast-steering mirror (FSM) cannot adapt to varying load demands. To address this issue, this paper presents an innovative variable-stiffness rotational mechanism designed for use with FSMs. Firstly, the working principle of the variable-stiffness mechanism is introduced, and the influence of the length of each structure on the stiffness and the nonlinear influence are analyzed. Then, the variable-stiffness mechanism is applied to the FSM for the variable-stiffness experiment and variable-load experiment. The experimental results show that the variable-stiffness mechanism designed in this paper can realize the change in stiffness. The errors between the experimental value and the theoretical value of the three sets of experiments are +5.72%, +7.57%, and +6.57%. The FSM’s stiffness nonlinearity is very small, and the resonance frequency of the FSM before and after increasing the load can be consistent. The variable-stiffness mechanism can change the frequency characteristics by changing the rotational stiffness of the FSM.

Optimal multi-layer economical schedule for coordinated multiple mode operation of wind–solar microgrids with hybrid energy storage systems

The aim of this paper is the design and implementation of an advanced model predictive control (MPC) strategy for the management of a wind–solar microgrid (MG) both in the islanded and grid-connected modes. The MG includes energy storage systems (ESSs) and interacts with external hydrogen and electricity consumers as an extra feature. The system participates in two different electricity markets, i.e., the daily and real-time markets, characterized by different time-scales. Thus, a high-layer control (HLC) and a low-layer control (LLC) are developed for the daily market and the real-time market, respectively. The sporadic characteristics of renewable energy sources and the variations in load demand are also briefly discussed by proposing a controller based on the stochastic MPC approach. Numerical simulations with real wind and solar generation profiles and spot prices show that the proposed controller optimally manages the ESSs, even when there is a deviation between the predicted scenario determined at the HLC and the real-time one managed by the LLC. Finally, the strategy is tested on a lab-scale MG set up at Khalifa University, Abu Dhabi, UAE.

Rotary Kiln Temperature Control Under Multiple Operating Conditions: an Error-Triggered Adaptive Model Predictive Control Solution

The temperature of rotary kiln, as one of the essential equipment in zinc smelting process, determines the product quality and resource utilization. However, since rotary kiln is a large-scale and highly coupled system, there are plenty of variables affecting the rotary kiln temperature, bringing too much redundant information to describe rotary kiln dynamics. In addition, due to the variations in raw materials, production load, and market demand, there exist various operation conditions, making it difficult to achieve stability control of the rotary kiln temperature with traditional control methods. To solve these problems, an error-triggered adaptive model predictive control (ET-AMPC) is proposed in this article. Specifically, since rotary kiln temperature is hard to regulate due to redundancy among variables and strong nonlinearity, an orthogonal maximum mutual information coefficient feature selection (OMICFS) method is first proposed to determine vital variables affecting temperature most. Then, aiming at the problem of changing operating conditions of rotary kilns, an ET-AMPC method is proposed, which can precisely adapt to different operating conditions and achieve stability control. Finally, experiments on a numerical simulation case and an industrial rotary kiln show the strength and reliability of the proposed method, which reduces 10%–20% trajectory tracking error in the period of operating condition changing and improves the control accuracy effectively.

Economic Viability of NaS Batteries for Optimal Microgrid Operation and Hosting Capacity Enhancement under Uncertain Conditions

Recent developments have increased the availability and prevalence of renewable energy sources (RESs) in grid-connected microgrids (MGs). As a result, the operation of an MG with numerous RESs has received considerable attention during the past few years. However, the variability and unpredictability of RESs have a substantial adverse effect on the accuracy of MG energy management. In order to obtain accurate outcomes, the analysis of the MG operation must consider the uncertainty parameters of RESs, market pricing, and electrical loads. As a result, our study has focused on load demand variations, intermittent RESs, and market price volatility. In this regard, energy storage is the most crucial facility to strengthen the MG’s reliability, especially in light of the rising generation of RESs. This work provides a two-stage optimization method for creating grid-connected MG operations. The optimal size and location of the energy storage are first provided to support the hosting capacity (HC) and the self-consumption rate (SCR) of the RESs. Second, an optimal constrained operating strategy for the grid-connected MG is proposed to minimize the MG operating cost while taking into account the optimal size and location of the energy storage that was formerly determined. The charge–discharge balance is the primary criterion in determining the most effective operating plan, which also considers the RES and MG limitations on operation. The well-known Harris hawks optimizer (HHO) is used to solve the optimization problem. The results showed that the proper positioning of the battery energy storage enhances the MG’s performance, supports the RESs’ SCR (reached 100% throughout the day), and increases the HC of RESs (rising from 8.863 MW to 10.213 MW). Additionally, when a battery energy storage system is connected to the MG, the operating costs are significantly reduced, with a savings percentage rate of 23.8%.

Control design and multimode power management of WECS connected to HVDC transmission line through a Vienna rectifier

This paper deals with nonlinear control and optimal management of wind energy conversion systems connected to HVDC transmission lines. Specifically, the energy system under study consists of a wind turbine driving a permanent magnet synchronous generator, the latter supplies energy to a power grid through a Vienna rectifier and a DC link. To compensate for intermittent nature of wind power and load demand variations, we design a voltage controller as well as a novel energy management strategy that accounts for the available wind power and DC load demands. In the considered management, distinction is made between two operation modes: i) the maximum power point tracking mode using an intelligent adaptive optimizer, where only electrical parameter measurements are used to ensure maximum power extraction; ii) the adaptive power point tracking mode employing demand-driven production. In both modes, the power factor requirement is ensured, regardless the weather conditions and load demand variations, enhancing thus the aerogenerator’s efficiency. One more novelty of the proposed control and management system is the online estimation of the DC voltage, avoiding thus the use of a physical sensor. The performances of the control and management system are highlighted by several simulations, showing the achievement all control objectives.

Comparison Analysis Between PI and Adaptive Controllers for DC-DC Converter of Hybrid Energy Storage Systems in Electric Vehicles

A power converter is one of the important components in a hybrid electric vehicle (HEV), where it has a strong nonlinear dynamic due to the variation of load demand from different driving modes, namely acceleration, braking and cruising. To adapt with the nonlinearities, this work proposes the use of direct model reference adaptive control (DMRAC) to regulate its operation in tracking the load and current demand of the HEV. To validate the response, the control performance is benchmarked with the commonly used traditional PI controller. The system model includes a battery with a supercapacitor, and its controller was constructed using the MATLAB Simulink platform. Simulation results show that DMRAC provides better performance as compared to the PI controller in two cases, which are tracking the current and load demands according to the root mean square error (RMSE) analysis. Nevertheless, in the presence of disturbance, it is noted that DMRAC is only effective in tracking the current demand while requiring some time to adapt and surpass the PI controller in tracking the load demand. Based on these findings, it can be justified that the DMRAC has the potential to become a good alternative approach to control the HEV power converters, specifically in the presence of disturbance.

Solutions of economic load dispatch problems for hybrid power plants using Dandelion optimizer

In this paper, an economic load dispatch problem (ELD) is solved for reaching optimal power output of hybrid systems in addition to cost minimization. The systems consider the forbidden working zones (FWZs), dynamic load demand, wind farms, and solar photovoltaic fields (SPs). The cost minimization solutions for the ELD problem are found by applying the Dandelion optimizer (DO), the salp swarm algorithm (SSA), and the particle swarm optimization (PSO). In the study case, the power system consists of six thermal power plants (TPs), two wind farms, and two SPs. In addition, the variation of load demand over 24 hours of one day is applied. DO and SSA can achieve the best cost of $15443.0753 for the first system, but PSO cannot. However, DO is the most stable method reaching the standard deviation of 0.0184 for fifty runs but that of SSA and PSO is about 1.0439 and 8.9664. For the second system, DO can reach smaller cost than PSO by $11.17, $137.74 and $323.09 for the best, mean and worst solutions among fifty found solutions. As a result, DO is strongly recommended for solving the ELD problem.

Nonsingular fast terminal sliding mode control for two-stage converters of AC island photovoltaic microgrid

Taking into account almost all kinds of variations and uncertainties to which AC island photovoltaic (PV) microgrid is often subjected, this paper proposes a new nonsingular fast terminal sliding mode control (NFTSMC) strategy for two-stage converters to enhance robustness against those disturbances and improve system dynamic performance. Firstly, to effectively decrease the adverse effect on the system from intermittent solar irradiation and uncertain environmental temperature, a NFTSMC cascaded with perturb and observe (P&O) scheme is put forward for fore-stage DC/DC converter, and PV output voltage is controlled to track its reference and thus maximum power point tracking (MPPT) performance is improved. Then, to significantly decrease the adverse effect on the system from variations of load and LC filter parameters, by incorporating an extended state observer (ESO) into NFTSMC, an ESO-based NFTSMC scheme is proposed for post-stage DC/AC converter, and the load voltage is regulated to track its reference and thus system robustness is strengthened, in which ESO is designed to replace load current sensor and thus reduce hardware cost. By using global terminal attractor without discontinuous switching function, the proposed NFTSMC strategy can not only strengthen system robustness but also improve system dynamic performance. Lastly, on account of the variations of PV generation and load demand, a conventional double-loop PI scheme is put into use for bidirectional DC/DC converter and thus system power balance is ensured. Comprehensive MATLAB simulation and StarSim/Hardware experiment results show that the proposed NFTSMC strategy for two-stage converters can overcome negative effects caused by various internal and external disturbances, and thus enable island PV microgrid to operate reliably and supply high-quality AC power.

Bellman–Genetic Hybrid Algorithm Optimization in Rural Area Microgrids

Incorporating renewable Distributed Energy Resources (DER) into the main grid is crucial for achieving a sustainable transition from fossil fuels. However, this generation system is complicated by the fluctuating behavior of renewable resources and the variable load demand, making it less reliable without a suitable energy storage system (ESS). This study proposes an Optimal Power Flow Management (OPFM) strategy for a grid-connected hybrid Micro Grid (MG) comprising a wind turbine (WT), a photovoltaic (PV) field, a storage battery, and a Micro Gas turbine (MGT). This proposed strategy includes (i) minimizing the MG’s daily energy cost, (ii) decreasing CO2 emissions by considering the variable load, weather forecast, and main grid fees to optimize the battery charging/discharging strategy, and (iii) optimizing the decision-making process for power purchase/sell from/to the main grid. The suggested OPFM approach is implemented using a Genetic Algorithm and compared with the Bellman Algorithm and a restricted management system via several simulations under the Matlab environment. Furthermore, the hybridization of the Bellman Algorithm and the Genetic Algorithm is proposed to enhance the OPFMC strategy’s efficiency by leveraging both algorithms’ strengths. The simulation results demonstrate the effectiveness of the proposed strategy in lowering energy costs and CO2 emissions and enhancing reliability. Additionally, the comparison of the hybridized GA algorithm reveals a cost 16% higher than the Bellman Algorithm; however, the use of the hybridized GA algorithm leads to a reduction in GHG emissions by 31.4%. These findings underscore the trade-off between cost and environmental impact in the context of algorithmic optimization for microgrid energy management.

Multi-objective control and optimization of a stand-alone photovoltaic power conversion system with battery storage energy management

This paper addresses the problem of controlling a stand-alone photovoltaic (PV) energy conversion system integrated with a battery energy storage system. The study focuses on a series association of PV panels, a DC/AC converter, a Li-ion battery, and a DC load. The intermittent nature of PV power and frequent variations in load demand decrease battery lifetime and its charging performance. To mitigate these issues, an efficient battery charge controller is proposed to instantaneously balance the PV power flow delivered to the DC load and the battery, ensuring optimal utilization of PV power and appropriate battery charging. Based on available solar power, battery state of charge (SOC), and DC load demand The controller adapts to three charging modes, namely, maximum power point tracking (MPPT) charging mode, constant current (CC) charging mode, and constant voltage (CV) charging mode. Additionally, a novel energy management algorithm is designed to ensure battery safety and determine the system’s mode of operation, considering weather conditions and load demand variations. Interestingly, no solar irradiation or battery SOC sensors are required for the implementation of the control system. Nonlinear and robust controllers are developed to provide the necessary control input laws for the management algorithm. The robustness and stability of the system are verified using the Lyapunov theory. Furthermore, the paper quantifies the performance of the proposed strategy through a comparative analysis using integral of absolute error (IAE) indices against two conventional control approaches. Simulation results validate the effectiveness of the proposed controller strategy, demonstrating its high performance and ability to meet the specified objectives. This work presents an innovative approach to enhance the efficiency and reliability of stand-alone PV energy conversion systems with battery storage, offering promising prospects for sustainable energy applications.

Load Frequency Control for Unequal Multi-Area Power System by Generation Control Strategy Using CFO (PR)2 Controller

An interconnected system is raised by integrating different generators in the power system of multiple areas that satisfies varying load demand. Frequency fluctuations occur in the system due to the interconnection of multiple generators. The voltage variations in any area may induce frequency variations in the system. Hence to solve the issue, a cascade fractional order proportional resonant (CFO(PR)2) controller is proposed in this work. The objective of a control model is to reduce the error function; hence the controller is tuned by a highly efficient optimization model named an average and subtraction-based optimizer (ASBO). The proposed controller’s results are compared with the existing controllers to check the performance efficacy. The comparative analysis shows that the proposed controller performs better regarding lower errors. Finally, the model is tested under four scenarios by randomly changing the load at a different area.

Integrating Demand Response for Enhanced Load Frequency Control in Micro-Grids with Heating, Ventilation and Air-Conditioning Systems

Heating, ventilation and air-conditioning (HVAC) systems constitute the majority of the demands in modern power systems for aggregated buildings. However, HVAC integrated with renewable energy sources (RES) face notable issues, such as uneven demand–supply balance, frequency oscillation and significant drop in system inertia owing to sudden disturbances in nearby generation for a longer period. To overcome these challenges, load frequency control (LFC) is implemented to regulate the frequency, maintain zero steady-state error between the generation and demand, reduce frequency deviations and balance the active power flow with neighboring control areas at a specified value. In view of this, the present paper investigates LFC with a proposed centralized single control strategy for a micro-grid (µG) system consisting of RESs and critical load of a HVAC system. The proposed control strategy includes a newly developed cascaded two-degree-of-freedom (2-DOF) proportional integral (PI) and proportional derivative filter (PDF) controller optimized with a very recent meta-heuristic algorithm—a modified crow search algorithm (mCSA)—after experimenting with the number of performance indices (PICs). The superiority of both the proposed optimization algorithm and the proposed controller is arrived at after comparison with similar other algorithms and similar controllers, respectively. Compared to conventional control schemes, the proposed scheme significantly reduces the frequency deviations, improving by 27.22% from the initial value and reducing the performance index criteria (ƞISE) control error to 0.000057. Furthermore, the demand response (DR) is implemented by an energy storage device (ESD), which validates the suitability of the proposed control strategy for the µG system and helps overcome the challenges associated with variable RESs inputs and load demand. Additionally, the improved robustness of the proposed controller for this application is demonstrated through sensitivity analysis with ±20% μG coefficient variation.

Optimal planning and forecasting of active distribution networks using a multi-stage deep learning based technique

This paper presents a comprehensive methodology for long-term planning in distribution networks to address the challenges associated with integrating renewable energy resources (RERs) and battery energy storage systems (BESSs). The methodology incorporates active network management (ANM) schemes, coordinated voltage control (CVC), and demand side management (DSM) to optimize the sizing and siting of RERs and BESS. Deep learning-based long-term forecasting techniques are utilized to capture the hourly variations in load demand, solar irradiance, and wind speed throughout the year. The objective is to minimize overall network costs, considering investment and operational costs, while planning over a 5-year horizon with a 3% annual load growth. The model includes tie-lines as a decision variable for network expansion investments, ensuring efficient network flexibility. The linearized mathematical model is developed as a mixed integer linear programming (MILP) problem to enhance scalability. A case study is conducted on the UKGDS 16-bus system to verify the optimization model, and further validation is performed on the IEEE 69-bus system to assess scalability. The results demonstrate the effectiveness of the proposed method in achieving accurate forecasting and efficient sizing and siting decisions for RERs and BESSs, including tie-line investments, providing valuable insights for real-world distribution network scenarios.

Precise Dynamic Modelling of Real-World Hybrid Solar-Hydrogen Energy Systems for Grid-Connected Buildings

Hybrid renewable hydrogen energy systems could play a key role in delivering sustainable solutions for enabling the Net Zero ambition; however, the lack of exact computational modelling tools for sizing the integrated system components and simulating their real-world dynamic behaviour remains a key technical challenge against their widespread adoption. This paper addresses this challenge by developing a precise dynamic model that allows sizing the rated capacity of the hybrid system components and accurately simulating their real-world dynamic behaviour while considering effective energy management between the grid-integrated system components to ensure that the maximum possible proportion of energy demand is supplied from clean sources rather than the grid. The proposed hybrid system components involve a solar PV system, electrolyser, pressurised hydrogen storage tank and fuel cell. The developed hybrid system model incorporates a set of mathematical models for the individual system components. The developed precise dynamic model allows identifying the electrolyser’s real-world hydrogen production levels in response to the input intermittent solar energy production while also simulating the electrochemical behaviour of the fuel cell and precisely quantifying its real-world output power and hydrogen consumption in response to load demand variations. Using a university campus case study building in Scotland, the effectiveness of the developed model has been assessed by benchmarking comparison between its results versus those obtained from a generic model in which the electrochemical characteristics of the electrolyser and fuel cell systems were not taken into consideration. Results from this comparison have demonstrated the potential of the developed model in simulating the real-world dynamic operation of hybrid solar hydrogen energy systems for grid-connected buildings while sizing the exact capacity of system components, avoiding oversizing associated with underutilisation costs and inaccurate simulation.

Near Real-Time Load Forecasting of Power System Using Fuzzy Time Series, Artificial Neural Networks, and Wavelet Transform Models

Due to the increasing usage of electrical power, the size of electrical power system has increased manifold over the years. There is no inventory or buffer from generation to customer; therefore, to provide a reliable and quality electrical energy whenever demanded, power utility engineers require an adequate, efficient, and precise load forecast to meet continuously varying load demands. This article presents the design and analysis of demand forecasting over shorter interval for power system. The fuzzy time series (FTS), artificial neural network (ANN), and wavelet transform (WT) based forecasting is presented and analyzed in this article. The real-time data from Indian utility is collected for forecasting the demand and to check the effectiveness of FTS, ANN, and WT. The various error definitions are used to calculate the accuracy of the proposed techniques, and the application results verify the superiority of WT and ANN over FTS by showing reduced error value with greater accuracy. Additionally, it is watched that wavelet db3, level 3 is discovered to be the most accurate Daubechies wavelet-oriented technique for predicting the demand in comparison to other dbs, and it highly aligns in reducing the error between actual and predicted demand.