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.

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.

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.

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.

Solar Energy Harvesting Using a Timer-Based Relay Selection

In this paper,the throughput and delay of cooperative communications are derived when solar energy is used and relay node is selected using a timer.The source and relays harvest energy from sun using a photo voltaic system.The harvested power is used by the source to transmit data to the relays.Then,a selected relay amplifies the signal to the destination.Opportunistic,partial and reactive relay selection are used.The relay transmits when its timer elapses.The timer is set to a value proportional to the inverse of its Signal to Noise Ratio(SNR).Therefore,the relay with largest SNR will transmit first and its signal will be detected by the other relays that will remain idle to avoid collisions.Harvesting duration is optimized to maximize the throughput.Packet’s waiting time and total delay are also computed.We also derive the statistics of SNR when solar energy is used.The harvested power from sun is proportional to the sum of a deterministic radiation intensity and a random attenuation due to weather effects and clouds occlusion.The fixed radiation intensity depends on season,month and time t in hour.The throughput of cooperative communications with energy harvesting from sun was not yet studied.

Optimal Location to Use Solar Energy in an Urban Situation

This study conducted in Lima, Peru, a combination of spatial decisionmaking system and machine learning was utilized to identify potentialsolar power plant construction sites within the city. Sundial measurementsof solar radiation, precipitation, temperature, and altitude were collectedfor the study. Gene Expression Programming (GEP), which is based on theevolution of intelligent models, and Artificial Neural Networks (ANN) wereboth utilized in this investigation, and the results obtained from each werecompared. Eighty percent of the data was utilized during the training phase,while the remaining twenty percent was utilized during the testing phase. Onthe basis of the findings, it was determined that the GEP is the most suitablenetwork for predicting the location. The test state’s Nash-Sutcliffe efficiency(NSE) was 0.90, and its root-mean-square error (RMSE) was 0.04. Followingthe generation of the final map based on the results of the GEP model, itwas determined that 9.2% of the province’s study area is suitable for theconstruction of photovoltaic solar power plants, while 53.5% is acceptable and37.3% is unsuitable. The ANN model reveals that only 1.7% of the study areais suitable for the construction of photovoltaic solar power plants, while 66.8%is acceptable and 31.5% is unsuitable.

Research on Operation Optimization of Heating System Based on Electric Storage Coupled Solar Energy and Air Source Heat Pump

For heating systems based on electricity storage coupled with solar energy and an air source heat pump(ECSA),choosing the appropriate combination of heat sources according to local conditions is the key to improving economic efficiency.In this paper,four cities in three climatic regions in China were selected,namely Nanjing in the hot summer and cold winter region,Tianjin in the cold region,Shenyang and Harbin in the severe cold winter region.The levelized cost of heat(LCOH)was used as the economic evaluation index,and the energy consumption and emissions of different pollutants were analyzed.TRNSYS software was used to simulate and analyze the system performance.The Hooke-Jeeves optimization algorithm and GenOpt software were used to optimize the system parameters.The results showed that ECSA systemhad an excellent operation effect in cold region and hot summer and cold winter region.Compared with ECS system,the systemenergy consumption,and the emission of different pollutants of ECSA system can be reduced by a maximum of 1.37 times.In cold region,the initial investment in an air source heat pump is higher due to the lower ambient temperature,resulting in an increase in the LOCH value of ECSA system.After the LOCH value of ECSA system in each region was optimized,the heating cost of the system was reduced,but also resulted in an increase in energy consumption and the emission of different pollutant gases.

Investment in Generation of Photovoltaic Solar Energy: A Fezsibility Study with Flexibility and Uncertainty

The objective of this research will be to calculate the feasibility of investing in a solar energy generation project through the development of a methodology that allows the capture of environmental uncertainties by improving decision making. The article presents a comparative study of the feasibility analysis of investment in a solar mini solar energy for a Shopping, considering a regime of certainty and uncertainty. The assumed stochastic variables were energy tariff and price of solar panels. The trajectories were simulated with the binomial approach that combined resulted in a quadratic diagram. The applied methodology presented the best recommendation and the option to wait was the most valuable. The exchange of the energy obtained from LIGHT by own generation of energy with solar photovoltaic source will be viable for the manager since it observes the behavior of the variables over time and follows the rules of optimal decision.

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.

A Solar Energy System Design for Green Hydrogen Production in South-Western Nigeria, Lagos State, Using HOMER & ASPEN

Solar system design for green hydrogen production has become the most prominent renewable energy research area, and this has also actively fueled the desire to achieve net-zero emissions. Hydrogen is a promising energy carrier because it possesses more energy capacity than fossil fuels and the abundant nature of renewable energy systems can be utilized for green hydrogen production. However, the design of an optimized electrical energy system required for hydrogen production is crucial. Solar energy is indeed beneficial for green hydrogen production and this research designed, discussed, and provided high-level research on HOMER design for green hydrogen production and deployed the energy requirement with ASPEN Plus to optimize the energy system, while also incorporating fuzzy logic and PID control approaches. In addition, a promising technology with a high potential for renewable hydrogen energy is the proton exchange membrane (PEM) electrolyzer. Since its cathode (hydrogen electrode) may be operated over a wide range of pressure, a control process must be added to the system in order for it to work dynamically efficiently. This system can be characterized as an analogous circuit that consists of a resistor, capacitor, and reversible voltage. As a result, this research work also explores the Fuzzy-PID control of the PEM electrolysis system. Both the PID and Fuzzy Logic control systems were simulated using the control simulation program Matlab R2018a, which makes use of Matlab script files and the Simulink environment. Based on the circuit diagram, a transfer function that represents the mathematical model of the plant was created, and the PEM electrolysis control system is determined to be highly significant and applicable to the two control systems. The PI controller, however, has a 30.8% overshoot deficit, but when the fuzzy control system is compared to the PID controller, it is found that the fuzzy control system achieves stability more quickly, demonstrating its benefit over PID.

The Benefits of Solar Energy on the Provision of Sustainable Affordable Housing in Nigeria

Solar energy is the most abundant form of energy on Earth. Solar energy brings impactful benefits and products that are expected to make homes more reliable, sustainable, and affordable. Thanks to technological advancements like the solar cell, we can gather this energy and turn it into electricity. The construction industry has an exceptional chance of benefiting from this sustainable energy. Many recognised benefits have been spelled forth in the construction industry, such as providing homes with clean energy with no trace of ozone depleting material emission. There are many people in Nigeria who are not linked to the public electric grid, and the energy sector produces and generates less than 58% of the entire amount of energy required. As stated in the Nigeria’s National Energy General Plan, the Sustainable Energy programme aims to enhance the country’s use of solar electricity. This paper focuses on the role of solar energy in the provision of sustainable affordable housing in Nigeria. It considers the description, method, and utilisation of solar energy with a focus on residential and commercial buildings.

An Introduction to Distribution-Level Solar Energy

Solar energy has been widely used in power generation.With the development of solar energy,the distributed photovoltaic power generation and the distributed grid-connected PV systems become the center of attention.This paper provided a brief introduction to distribution-level solar energy.Firstly,the development of solar energy was analyzed,and the distributed photovoltaic power generation was discussed.Secondly,the distributed grid-connected PV systems and basic theory of photovoltaic solar channel were analyzed.In order to ensure PV power is connected to grid stably and reliably,some related aspects such as the establishment of mathematical model for solar photovoltaic cell,the analysis of I-V characteristics of solar photovoltaic cell,and the tracking of its maximum power point(MPPT)to control the behaviour of the DC/DC converter were discussed.Finally,a simulation model was necessary to be established by using PSCAD/EMTDC function module to verify and simulate the mathematical model and control strategies,and some suggestions were put forward for the sustainable development of solar energy.

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.

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 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 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 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.

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.