Assessing Factors Influencing Solar Energy Adoption among Small and Medium Enterprises in Mzuzu City, Malawi

One of the many renewable energy sources that offer advantages is solar energy, which also lowers energy prices and promotes environmental sustainability and energy security. Despite these advantages, various barriers, such as installation costs, have prevented small and medium-sized enterprises from investigating this invention. Malawi has a significant energy shortfall such that most businesses have been hindered from their profit maximization goals. The “Photovoltaic systems” (PV) that transform sunlight into electricity are the subject of this study. This type of solar energy system is situated on the building’s roof and generally produces electricity for businesses and even homes. Solar energy offers a great impact to small and medium enterprises in Mzuzu city with a cost-effective and dependable alternative to energy that has the potential to change the game. Therefore the aim of the study was to identify factors that encourage the adoption of solar energy among small medium enterprises in the city of Mzuzu city. And to identify some of barriers faced when adopting solar energy among small and medium enterprises in the city of Mzuzu. The research approach employed in the study was a survey. A survey is a type of research methodology in which primary data is gathered from a sample using a questionnaire. When information is to be gathered from a wider sample, a survey is employed. A bigger sample size was needed in this study in order to facilitate hypothesis testing. It is advised to apply a logical approach while using the survey. The survey utilized a five-point Likert scale. The study used convenience sampling to select study participants. The sample size in this study was determined using Cochran’s sample size formula. Statistical Package for Social Sciences (SPSS) and Microsoft Excel were used for statistical analysis. About 97.2% of the participants were aware of solar as a source of energy compared to 2.8 % who were unaware. The majority of participants use solar energy for lighting only, seconded by those who use electricity. The least number of participants use solar energy for cooling only. The majority of participants 21.5% indicated partnership and collaboration as the most motivating factor for the adoption of solar energy. This was followed by technical expertise 19.1 % the least number of participants 10.8% expressed that policy and regulatory frameworks were associated with the adoption of solar energy. This study found that there are no statistically significant factors influencing barriers to the adoption of solar energy. The price of solar energy adoption was identified as the least factor associated with the acceptance or rejection of solar energy. Nonetheless, the reasons given by the homes that had embraced solar technology aligned with the findings of other studies. This survey also found that although the public was aware of solar energy, and technology, there were still a number of factors that mattered, especially for non-adopters.

Mixing characteristics of three-cylinder valve-controlled energy recovery device based on liquid piston

The isobaric energy recovery device can significantly reduce the energy consumption of the seawater reverse osmosis system by recycling the residual pressure energy of high-pressure concentrated brine.Three-cylinder valve-controlled energy recovery device(TC-ERD)solves the fluid pulsation of traditional two-cylinder devices,but the use of a“liquid piston”exacerbates the mixing between brine and seawater.Herein,the evolutionary law of“liquid piston”and the relationship between volumetric mixing degree and operating conditions are explored.The results show that the“liquid piston”first axially expands and then gradually stabilizes,isolating the brine and seawater.Additionally,as long as the volume utilization ratio(U_(R))of the pressure exchange cylinder remains constant,there will not be much difference in the volumetric mixing degree after stabilization of the“liquid piston”(Vm-max)regardless of changes in the processing capacity(Q)and cycle time(T_(0)).Therefore,the equation for Vm-max with respect to the operating parameters(Q,T_(0))is derived,which can not only predict the Vm-max of the TCERD,but also provide an empirical reference for the design of other valve-controlled devices with“liquid piston”.When the Vm-max is 6%,the efficiency of the TC-ERD at design conditions(30 m^(3)·h^(-1),5.0 MPa)is 97.53%.

Enhancing Solar Energy Production Forecasting Using Advanced Machine Learning and Deep Learning Techniques: A Comprehensive Study on the Impact of Meteorological Data

The increasing adoption of solar photovoltaic systems necessitates accurate forecasting of solar energy production to enhance grid stability,reliability,and economic benefits.This study explores advanced machine learning(ML)and deep learning(DL)techniques for predicting solar energy generation,emphasizing the significant impact of meteorological data.A comprehensive dataset,encompassing detailed weather conditions and solar energy metrics,was collected and preprocessed to improve model accuracy.Various models were developed and trained with different preprocessing stages.Finally,three datasets were prepared.A novel hour-based prediction wrapper was introduced,utilizing external sunrise and sunset data to restrict predictions to daylight hours,thereby enhancing model performance.A cascaded stacking model incorporating association rules,weak predictors,and a modified stacking aggregation procedure was proposed,demonstrating enhanced generalization and reduced prediction errors.Results indicated that models trained on raw data generally performed better than those on stripped data.The Long Short-Term Memory(LSTM)with Inception layers’model was the most effective,achieving significant performance improvements through feature selection,data preprocessing,and innovative modeling techniques.The study underscores the potential to combine detailed meteorological data with advanced ML and DL methods to improve the accuracy of solar energy forecasting,thereby optimizing energy management and planning.

Cascade utilization of full spectrum solar energy for achieving simultaneous hydrogen production and all-day thermoelectric conversion

Solar-driven photocatalytic water/seawater splitting holds great potential for green hydrogen production.However,the practical application is hindered by the relatively low conversion efficiency resulting from the inadequate utilization of solar spectrum with significant waste in the form of heat.Moreover,current equipment struggles to maintain all-day operation subjected to the lack of light during nighttime.Herein,a novel hybrid system integrating photothermal catalytic(PTC)reactor,thermoelectric generator(TEG),and phase change materials(PCM)was proposed and designed(named as PTC-TEG-PCM)to address these challenges and enable simultaneous overall seawater splitting and 24-hour power generation.The PTC system effectively maintains in an optimal temperature range to maximize photothermal-assisted photocatalytic hydrogen production.The TEG component recycles the low-grade waste heat for power generation,complementing the shortcoming of photocatalytic conversion and achieving cascade utilization of full-spectrum solar energy.Furthermore,exceptional thermal storage capability of PCM allow for the conversion of released heat into electricity during nighttime,contributing significantly to the overall power output and enabling PTC-TEG-PCM to operate for more than 12 h under the actual condition.Compared to traditional PTC system,the overall energy conversion efficiency of the PTC-TEG-PCM system can be increased by∼500%,while maintaining the solar-to-hydrogen efficiency.The advancement of this novel system demonstrated that recycling waste heat from the PTC system and utilizing heat absorption/release capability of PCM for thermoelectric application are effective strategies to improve solar energy conversion.With flexible parameter designing,PTC-TEG-PCM can be applied in various scenarios,offering high efficiency,stability,and sustainability.

Solar Energy Resource Characteristics of Photovoltaic Power Station in Shandong Province

[Objective] The aim was to analyze characters of solar energy in photo- voltaic power stations in Shandong Province. [Method] The models of total solar radiation and scattered radiation were determined, and solar energy resources in pho-tovoltaic power stations were evaluated based on illumination in horizontal plane and cloud data in 123 counties or cities and observed information in Jinan, Fushan and Juxian in 1988-2008. [Result] Solar energy in northern regions in Shandong proved most abundant, which is suitable for photovoltaic power generation; the optimal angle of tilt of photovoltaic array was at 35°, decreasing by 2°-3° compared with local latitude. Total solar radiation received by the slope with optimal angle of tilt exceeded 1 600 kw.h/（m2.a）, increasing by 16% compared with horizontal planes. The maximal irradiance concluded by WRF in different regions tended to be volatile in 1 020-1 060 W/m2. [Conclusion] The research provides references for construction of photovoltaic power stations in Shandong Province.

Analysis of Current Research and Future Development Trends of Applying Solar Energy in Street Lighting

In the context of promoting green energy transition and addressing climate change globally,solar energy,as a clean and renewable energy source,has gradually become a hot topic for research.Solar streetlight systems realize energy self-sufficiency and environment-friendly lighting by integrating photovoltaic power generation technology and efficient LED lighting technology.By comprehensively analyzing the current status of the application of solar streetlights at home and abroad,this paper discusses its technical advantages,market penetration,and challenges in its development.In terms of technical characteristics,this paper focuses on analyzing the key technologies such as energy conversion efficiency and intelligent control systems of solar streetlights.

Research on Solar Cell with Storing Solar Energy Film

Solar cell is an effective apparatus which can transform solar energy into electrical energy. However, the main problem is the low density and discontinuity of solar energy at present. The solar cell with a layer of rare earth film can absorb incidence sunlight and enhance the energy density of solar energy. The rare earth film can absorb solar energy and bear high temperature of 300～450 ℃. Moreover, in rainy days or at night, the film radiates the solar energy it stored in 8～12 h, so that the solar cell can work continuously, which remarkably enhanced the efficiency of solar cell.

Solar energy conversion on g-C3N4 photocatalyst:Light harvesting,charge separation,and surface kinetics

Photocatalysis. which utilizes solar energy to trigger chemical reactions, is one of the most desirable solar-energy-conversion approaches. Graphitic carbon nitride （g-C3N4）. as an attractive metal-free photocatalyst, has drawn worldwide research interest in the area of solar energy conversion due to its easy synthesis, earth-abundant nature, physicochemical stability and visible-light-responsive properties. Over the past ten years, g-C3N4 based photocatalysts have experienced intensive exploration, and great progress has been achieved. However, the solar conversion efficiency is still far from industrial applications due to the wide bandgap, severe charge recombination, and lack of surface active sites. Many strategies have been proposed to enhance the light absorption, reduce the recombination of charge carriers and accelerate the surface kinetics. This work makes a crucial review about the main contributions of various strategies to the light harvesting, charge separation and surface kinetics of g-C3N4 photocatalyst. Furthermore, the evaluation measurements for the enhanced light harvesting, reduced charge recombination and accelerated surface kinetics will be discussed. In addition, this review proposes future trends to enhance the photocatalytic performance of g-C3N4 photocatalyst for the solar energy conversion.

Solar Energy System with Digital Controller for Grid Connected Systems

--The solar photovoltaic （PV） module output voltage changes according to the variation of light intensity and temperature. This paper presents the implementation of an automatic digital controller of a DC-DC boost converter without batteries for a solar cell module by using a peripheral interface controller, which forms a closed loop, to control the ON-OFF period of the switching pulse. The output of DC-DC converter is maintained by automatically increasing or decreasing the pulse width. To produce the pulse width modulation （PWM）, the microcontroller is programmed according to the required duty cycle for the power switch. The PWM ON period is increased with the decrease in the PV voltage and vice-versa. The input voltage to the inverter is maintained constantly and is converted into an AC signal by using the metal-oxide-semiconductor field effect transistor （MOSFET） H-bridge operated in the sinusoidal pulse width modulation mode by using a PIC （peripheral interface controller） microcontroller. The generated AC signal can be connected to the AC grid or to the AC load. The simulated results by using Proteus 8 and hardware implemented results verify the effectiveness of the proposed controller.

Solar Energy Cell with Rare Earth Film

The characteristic of the solar energy cell with the rare earth film according to theory of molecular structure was introduced.When sunlight shines, the molecules of the rare earth film can absorb energy of the photon and jump to the excited state from the basic state, and play a role in storing solar energy.When sunlight do not shine, the electron of the excited state returns to the basic state, the rare earth film can automatically give out light and shine to surface of the solar cell, which can make solar cell continuously generate electric current.The rare earth film can absorb direct,scattering sunlight, and increase density of solar energy to reach surface of the solar cell, and play focusing function.The rare earth film can bear 350 ~ 500 ℃, which make the solar cell be able to utilize the focusing function system.Because after luminescence of the rare earth film, it can release again the absorbed solar energy through 1 ~ 8 h, and play a role in storing solar energy; The solar cell with the rare-earth film can generate electricity during night and cloudy days, and remarkably increase efficiency of the solar cell.

Solar Energy Prospects in Bangladesh: Target and Current Status

Owing to the favorable geographical location, Bangladesh captures a good amount of solar radiation per day. The proper utilization of this solar energy may reduce the country’s energy demand to a great extent. Bangladesh government has already made a master plan to utilize the abundant solar energy in different ways with a capacity development target of 600 MW by the end of 2021. Until 2018 a total capacity of 220 MW of solar power could be achieved by installing 6.9 million solar home systems (SHSs). On the other way, rooftop solar and solar mini-grid projects facilitated the capacity of 3.07 MW and 5 MW, respectively. A capacity of 32 MW could also be touched by solar irrigation projects with more than 1300 pumps for serving country’s rural people, and solar-diesel hybrid solution program (by installing 138 small power stations) has been supporting the telecom operators. Bangladesh power development board (BPDB), and Infrastructure Development Company Limited (IDCOL) have been promoting numerous research-development solar projects to many governments and private universities to build sustainable energy equipped country.

Hyperparameter Optimization Based Deep Belief Network for Clean Buses Using Solar Energy Model

Renewable energy has become a solution to the world’s energy concerns in recent years.Photovoltaic(PV)technology is the fastest technique to convert solar radiation into electricity.Solar-powered buses,metros,and cars use PV technology.Such technologies are always evolving.Included in the parameters that need to be analysed and examined include PV capabilities,vehicle power requirements,utility patterns,acceleration and deceleration rates,and storage module type and capacity,among others.PVPG is intermit-tent and weather-dependent.Accurate forecasting and modelling of PV sys-tem output power are key to managing storage,delivery,and smart grids.With unparalleled data granularity,a data-driven system could better anticipate solar generation.Deep learning(DL)models have gained popularity due to their capacity to handle complex datasets and increase computing power.This article introduces the Galactic Swarm Optimization with Deep Belief Network(GSODBN-PPGF)model.The GSODBN-PPGF model predicts PV power production.The GSODBN-PPGF model normalises data using data scaling.DBN is used to forecast PV power output.The GSO algorithm boosts the DBN model’s predicted output.GSODBN-PPGF projected 0.002 after 40 h but observed 0.063.The GSODBN-PPGF model validation is compared to existing approaches.Simulations showed that the GSODBN-PPGF model outperformed recent techniques.It shows that the proposed model is better at forecasting than other models and can be used to predict the PV power output for the next day.

CT-NET: A Novel Convolutional Transformer-Based Network for Short-Term Solar Energy Forecasting Using Climatic Information

Photovoltaic(PV)systems are environmentally friendly,generate green energy,and receive support from policies and organizations.However,weather fluctuations make large-scale PV power integration and management challenging despite the economic benefits.Existing PV forecasting techniques(sequential and convolutional neural networks(CNN))are sensitive to environmental conditions,reducing energy distribution system performance.To handle these issues,this article proposes an efficient,weather-resilient convolutional-transformer-based network(CT-NET)for accurate and efficient PV power forecasting.The network consists of three main modules.First,the acquired PV generation data are forwarded to the pre-processing module for data refinement.Next,to carry out data encoding,a CNNbased multi-head attention(MHA)module is developed in which a single MHA is used to decode the encoded data.The encoder module is mainly composed of 1D convolutional and MHA layers,which extract local as well as contextual features,while the decoder part includes MHA and feedforward layers to generate the final prediction.Finally,the performance of the proposed network is evaluated using standard error metrics,including the mean squared error(MSE),root mean squared error(RMSE),and mean absolute percentage error(MAPE).An ablation study and comparative analysis with several competitive state-of-the-art approaches revealed a lower error rate in terms of MSE(0.0471),RMSE(0.2167),and MAPE(0.6135)over publicly available benchmark data.In addition,it is demonstrated that our proposed model is less complex,with the lowest number of parameters(0.0135 M),size(0.106 MB),and inference time(2 ms/step),suggesting that it is easy to integrate into the smart grid.

Simple DSP Implementation of Maximum Power Pointer Tracking and Inverter Control for Solar Energy Applications

In this paper, a simple Digital Signal Processor (DSP) based Maximum Power Pointer Tracking (MPPT) control and Inverter Control is presented for solar energy applications, especially photovoltaic and wind energy systems. The proposed MPPT controlled boost converter is able to reduce the inrush current and the overshoot of the output voltage of the system. Details of the proposed Maltab-Simulink based MPPT and Inverter Control are shown and implemented using a DSP. The proposed system is analyzed and simulated for verification. To validate the system, a 100 W prototype test-bed is built and tested. The results show that the proposed system can be applicable for solar energy applications.

Landsat Time Series Analysis of Vegetation Changes in Solar Energy Development Areas of the Lower Colorado Desert, Southern California

Land cover change from renewable energy development in southern California is receiving increasing attention due to potential impacts on protected area conservation, endangered species, and greenhouse gas emissions. This study was designed to quantify and map, for the first time, variations desert vegetation canopy density and related growth rates using 30 consecutive years of Landsat satellite image data across the Lower Colorado Desert. The time-series for mean normalized difference vegetation index (NDVI) values sampled from each of the three major land cover types (shrubland, barren sand dune, and developed urban) showed no significant positive or negative trend in vegetation canopy density. Three periods of significant decrease in NDVI were detected during the drought periods of 1989-1990, 2002-2003, and 2013-2015, indicating that annual precipitation has been the main controller of shrubland canopy growth and green cover. Shrubland canopy cover has been relatively stable in renewable energy development zones since the mid-2000s. NDVI change in the period after nearly all southern California solar energy developments were initiated (post-2010) could be attributed largely to topographic water flow pathways through canyons and desert washes, both in and around all solar energy development zones.

Hollow semiconductor photocatalysts for solar energy conversion

The development of high-efficient photocatalysts plays an important role in the sustainable utilization of solar energy.Hollow nanostructured photocatalysts are vital for solar light utilization and charge carrier separation in photocatalytic processes.Therefore,the construction of hollow semiconductor photocatalysts is a promising strategy for preparing novel high-efficient photocatalysts.This paper reviews common hollow semiconductor nanomaterials,such as oxides,sulfides,nitrides,C_(3)N_(4),MOFs,and their composite photocatalysts.The characteristics of hollow-structure photocatalysts,the application of solar energy conversion,and their understanding of the photocatalytic mechanism are also reviewed.In addition,future challenges will be focused on designing and majorizing broadband response hollow-structure photocatalysts to further enhance solar energy conversion.Hollow semiconductor photocatalysts will have potential applications in the natural environment,and these synthesized strategies can also provide new possibilities for synthesizing other high-performance semiconductor photocatalysts.

Application of Hopfield Neural Networks Approach in Solar Energy Product Conceptual Design

A new product conceptual design approach is put forward based on Hopfield neural networks models. By research on the mechanisms of Hopfield neural networks, the associative simulation approaches are proposed. The approach is given by Hebb learn- ing law, Hopfield neural networks and crossover and mutation. The calculating models and the calculating formulas for the concep- tual design are put forward. Finally, an example for the conceptual design of a solar energy lamp is given. The better results are ob- tained in the conceptual design.

RESIDENTIAL SOLAR ENERGY POTENTIAL FOR PUBLIC DISSEMINATION:A CASE STUDY IN CONCEPCION,CHILE

This paper presents a method for estimating the solar capture capacity of dwell-ings using the central urban area of Concepción,Chile,as a case study in order to promote self-generation of energy by residents.The method takes into account the growing domestic energy demand and the possibility of meeting this demand through integrated solar energy collection into buildings using different systems.The methodology considers a study of the potential incoming solar radiation on buildings according to their geographical location and the surrounding buildings.The capacity for solar capture is then estimated for different dwelling types accord-ing to their morphology.Subsequently,the energy contribution provided by differ-ent technologies(solar thermal,photovoltaic and hybrid)is identified in relation to the main average energy demands for electricity,water and space heating.Finally,systems for each dwelling are recommended in an urban map available online.The development is based on climate information,cartography,aerial photo-graphs,surveys,housing models,technical standards,standardised calculations and dynamic simulations,implemented according to building layouts from an online Geographic Information System(GIS).The housing types are categorised in an urban map that relates household demands and the contribution of different solar energy systems.According to the estimates calculated,the residential units in the study offer sufficient solar capacity to supply between 40 and 60%of their energy consumption,especially in detached houses using roof-mounted hybrid systems.

Thermodynamic Investigation of a Solar Energy Cogeneration Plant Using an Organic Rankine Cycle in Supercritical Conditions

In the present work,a novel Organic Rankine Cycle(ORC)configuration is used for a low-grade heat source cogeneration plant.An investigation is conducted accordingly into the simultaneous production of electricity and cold.The proposed configuration relies on concentrated solar power(as heat source)and ambient air(for cooling).Furthermore,two gas ejectors are added to the system in order to optimize the thermodynamic efficiency of the organic Rankine cycle.The results show that the thermodynamic and geometric parameters related to these ejectors have an important effect on the overall system performances.In order to account for the related environmental impact,the following working fluids are considered:HCFC-124,HFC-236fa,HFO-1234yf and HFO-1234ze.As shown by the numerical simulations,the fluid R1234yf presents the minimal heat consumption and therefore provides an optimal thermal efficiency for the ORC cycle(which is around 29%).However,the refrigerant R236fa displays the highest refrigeration performances with a performance coefficient reaching a value as high as 0.38.

Utilization of Solar Energy in Irrigation Systems in Bangladesh

The demand for water pumping in urban water supply and irrigation in Bangladesh is significantly influenced by electricity deficits and high diesel costs. To address these challenges, the adoption of solar power for water pumping emerges as a viable alternative to traditional systems reliant on grid power and diesel. In recent years, there has been a growing emphasis on clean and renewable energies, aligning with the environmental and economic priorities of Bangladesh. The agricultural sector, serving as the backbone of the country’s economy, witnesses an escalating demand for water as the population increases. The extraction and transfer of water for agricultural and drinking purposes translate to high-energy consumption. Leveraging the abundant and essentially free solar energy, particularly during the crop growth periods when irrigation is crucial, presents an optimal solution. This study underscores the underutilization of this vital resource in Bangladesh and advocates for the widespread implementation of solar energy conversion programs, specifically in photovoltaic pumping systems. By comparing these systems with conventional diesel pumps, this paper aims to inspire policymakers, statesmen, and industry professionals to integrate green energy into the water sector. The envisioned outcome is a strategic shift towards sustainable development, with a focus on harnessing solar power to pump water for villages and agriculture, thus contributing to economic and environmental sustainability.