This thesis focuses on the search for neutrinos from the solar hep reaction using the combined three phases of the Sudbury Neutrino Observatory (SNO) data. The data were taken over the years 19992006, totalling 1,083 days of live neutrino time. The previous published SNO hep neutrino search was completed in 2001 and only included the first phase of data taking. That hep search used an event counting approach in one energy bin with no energy spectral information included. This thesis will use a spectral analysis approach. The hep neutrino search will be a Bayesian analysis using Markov Chain Monte Carlo (MCMC), and a Metropolis-Hastings algorithm to sample the likelihood space. The method allows us to determine the best fit values for the parameters. This signal extraction will measure the 8B flux, the atmospheric neutrino background rate in the SNO detector, and the hep flux. This thesis describes the tests used to verify the MCMC algorithm and signal extraction. It defines the systematic uncertainties and how they were accounted for in the fit. It also shows the correlations between all of the parameters and the effect of each systematic uncertainty on the result. The three phase hep signal extraction was completed using only 1/3 of the full data set. With these lowered statistics, this analysis was able to place an upper limit on the hep flux of 4.2 10^4 cm2 s1 with a 90% confidence limit. It was able to measure a hep flux of (2.40(+1.19)(-1.60))10^4 cm2 s1. These numbers can be compared with the previous SNO upper limit of 2.310^4 cm2 s1 with a 90% confidence limit, and the standard solar model prediction of (7.970 1.236) 10^3 cm2 s1.

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The amount of force or power when applied can move one object from one position to another or the capacity of a system to do work is called energy. It exists in everybody whether they are human beings or animals or non-living things. There are many forms of energy such as: kinetic, potential, light, sound, gravitational, elastic, electromagnetic or nuclear. According to the law of conservation of energy, any form of energy can be converted into another form and the total energy will remain the same. Energy can be broadly classified into two main groups’ i.e. renewable and nonrenewable resources. Many of the renewable energy a technology have been around for years, and as the time go by, are increasing in efficiency. Keywords: solar panel improvement, motor control, energy –efficient Solarplatten, Motosteuerung, Energieeffizenz

The amount of force or power when applied can move one object from one position to another or the capacity of a system to do work is called energy. It exists in everybody whether they are human beings or animals or non-living things. There are many forms of energy such as: kinetic, potential, light, sound, gravitational, elastic, electromagnetic or nuclear. According to the law of conservation of energy, any form of energy can be converted into another form and the total energy will remain the same. Energy can be broadly classified into two main groups’ i.e. renewable and nonrenewable resources. Many of the renewable energy a technology have been around for years, and as the time go by, are increasing in efficiency. Keywords: solar panel improvement, motor control, energy –efficient Solarplatten, Motosteuerung, Energieeffizenz

The global climate and energy crisis has amplified the need for renewable energy sources, withsolar photovoltaic (PV) systems expected to play a significant role in the future energy mix. In this context, distributed energy systems (DES) are identified as part of the solution to address climate and energy challenges.With the increasing demand for photovoltaic energy sources, there is a growing requirement forefficient Business Case Tools (BCT) to analyze investments in distributed solar PV installations.A two-part model, consisting of a solar model and spot price data, was developed based onparameters such as solar radiation, location, angle, orientation, system losses, installedcapacity, and historical spot price data. The model was integrated with Power BI for investment calculations and visualization of results. The developed model provides approximations for solar PV system electricity production, which were validated against selected installations in allelectricity areas of Sweden. The validation revealed an average relative absolute error of 14.72 percent for the model. The conclusion drawn is that BCT can be utilized to analyze and visualize solar PV investments at specific locations in Sweden. The results indicate that Power BI, as a BCT, has limitations indynamic data collection but performs well in executing calculation of investments and visualizingthe results. Well-developed BCT can facilitate decision-making through real-time calculations and contribute to smoother implementation of distributed systems by providing detailed insightsinto their financial characteristics. Further research is needed to develop a model specificallytailored for distributed installations with storage capabilities. / Världen befinner sig i en global klimat- och energikris vilket ökat behovet av och efterfrågan på förnybara energikällor. Solceller förväntas utgöra en betydande del av den framtida energimixen. I kombination med detta identifieras distribuerade energisystem (DES) som endel av lösningen på klimat- och energifrågan. I takt med den ökade efterfrågan på fotovoltaiska energikällor ställs större krav på effektiva Business Case Tools (BCT) för att analysera investeringar i distribuerade solcellsanläggningar. En modell bestående av två delar, en solmodell och spotprisdata,utvecklades utifrån parametrarna solstrålning, plats, vinkel, riktning, systemförluster, installerad effekt samt historiska spotprisdata. Modellen sammankopplas med Power BI föratt utföra investeringskalkyler och visualisera resultatet. Den utvecklade modellen gerapproximationer för solcellsanläggningars elproduktion, vilket validerades mot utvaldaanläggningar i Sveriges samtliga elområden. Enligt valideringen uppgår modellens genomsnittliga relativa absoluta fel till 14,72 procent. Slutsatsen dras att BCT kan användas för att analysera och visualisera solcellsinvesteringar på specifika platser i Sverige. Resultatet visar att Power BI som BCT har brister när detkommer till dynamisk datainsamling, men genomför och visualiserar investerings kalkyler med enkelhet. Välutvecklade BCT kan användas för att underlätta beslutsfattande genomrealtidsberäkningar och kan bidra till en smidigare implementering av distribuerade systemgenom att belysa deras finansiella karaktär på ett detaljerat sätt. Fortsatt forskning krävs föratt ta fram en modell anpassad för distribuerade anläggningar med lagringsmöjligheter.