https://www.um.edu.mt/library/oar/handle/123456789/6578 Thu, 09 Apr 2026 02:28:24 GMT 2026-04-09T02:28:24Z https://www.um.edu.mt/library/oar/handle/123456789/144597 Title: A study on the effect of thin object shading on the performance of photovoltaic modules Abstract: This study investigates a specific and underexplored aspect of partial shading of photovoltaic (PV) systems that is caused by thin horizontal objects. Following a review of both fundamental principles and current state-of-the-art, the primary aims were to examine differences in shadow formation and associated power loss between laboratory and outdoor environments, quantify shadow formation characteristics, and to assess the scientific validity of commonly used simplifications in representing penumbra shadows. This study presents a comparative analysis of thin object shadows under laboratory environment conditions using a solar simulator, and under outdoor environmental conditions. Using a 156.0 × 156.0 mm solar cell, 56 thin object shading cases were compared using thin objects of thicknesses of ⌀2.0 mm - ⌀20.0 mm at distances between 10 cm – 40 cm in 5 cm increments. The solar cell placed in outdoor conditions produced higher power losses in the range of 0.34 W - 0.67 W, equivalent to 8.18% to 16.13% power loss respectively, when converted to standard test conditions (STC). This observation was validated by producing a quantitative factor which incorporated both the size and intensity of the overall shadow at equal weight where clearly the outdoor environment produced stronger shadow formation. Moreover, the image capture of all 56 cases found evidence of the laboratory environment producing fringed shadow patterns caused by Fresnel diffraction on the thin objects, most evidently in the shadows of the smallest thin object (⌀2 mm). This thesis also implemented a novel methodology to quantify the shadow formation and the power loss of 104 thin object shading cases, using custom made image analysis tool named ThinShadePV. This tool utilises image analysis and Random Forest machine learning to determine the size and intensity of umbra and penumbra shadow regions from captured shadow images and predict the associated power losses. Validation through 15 real-world thin shadow cases demonstrated strong agreement between predicted and actual power loss values, with a mean deviation of just 0.48% and an accuracy range of –3.68% to +2.71%. Moreover, Spearman correlation and sensitivity analyses revealed that penumbra intensity is the most influential factor for smaller objects (⌀2.8–⌀8.0 mm thick), while the size of the umbra shadow dominates in larger objects (⌀10–⌀16.0 mm) thick. The study also assessed the validity of approximating varying-intensity penumbra shadows with constant-intensity penumbra representations using neutral density (ND) filters. While constant-intensity shadows closely replicated the relationship profile, they consistently overestimated power loss, despite matching the size and average varying intensity shadows. A large effect size (Cohen’s d = –0.893) confirmed a significant discrepancy, indicating that varying-intensity penumbra shadows should not be simplified to constant-intensity models in analytical or simulation contexts. One of the key contributions of this study lies in its significant advancement of current knowledge by empirically quantifying the effects of horizontal thin object shading on PV performance and establishing key relationships between shading parameters and power loss. The development of ThinShadePV, a purpose-built image analysis tool, enables quantitative measurement of shadow characteristics and power loss prediction. Moreover, ThinShadePV holds strong potential for integration into commercial PV simulation software, enhancing the accuracy of performance forecasts under real-world shading conditions. Description: Ph.D.(Melit.) Wed, 01 Jan 2025 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/144597 2025-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/141615 Title: On the use of measure-correlate-predict methodologies and energy demand forecasting to assess energy storage capabilities for offshore wind farms Abstract: Energy storage is crucial for the continued penetration of renewable energy. One of the most important reasons for this is that, for a given point of time, the availability of renewable energy resources rarely matches the demand for electrical energy. The integration of offshore windfarms with energy storage facilities, requires a capital-intensive investment which can only be justified by an adequate return on investment (ROI). Currently, Measure-Correlate-Predict (MCP) analysis is used to assess the viability of offshore windfarms while energy demand forecasting is normally used to manage and plan the electricity grid infrastructure. This research combined wind energy prediction methodologies with Energy Demand Forecasting (EDF) methodologies to size the energy storage capacity for an offshore windfarm and evaluated the economic feasibility. This research analysed various regression techniques for MCP analysis. Data from a Light Detection and Ranging (LiDAR) system were utilised. The study was extended to analyse the behaviour of a hypothetical floating windfarm, situated off the Northern Coast of the Island of Malta. The effect of using the different regression techniques for MCP analysis on the power output from the windfarm could therefore be evaluated. The second part of the research used a combination of ARIMA and regression techniques to forecast the energy demand over several years. The output from the windfarm was applied to a model which integrated the said windfarm to an Energy Storage System (ESS) and the electricity grid. Measurement matrices were used to compare the behaviour of the combined windfarm, ESS and electricity grid, based on the actual and predicted data from the various regression techniques used for the MCP analysis and EDF. This created a matrix of results which was used to determine the optimal combination of regression techniques used for MCP analysis and EDF, following which, the optimal capacity of the ESS was established. The long-term behaviour of the windfarm and the of the energy storage system were also predicted. The Levelised Cost of Energy (LCOE) for the windfarm and the Levelised Cost of Storage (LCOS) for the Energy Storage System were also calculated, using different windfarm scenarios, and analysing the error due to the use of the MCP and EDF methodologies. This research therefore established a methodology for combining MCP and EDF to determine the optimal capacity of an ESS which was coupled to an offshore windfarm and the electricity grid. The error in establishing this capacity was determined. The end result was the determination of the LCOE of the windfarm and the LCOS of the ESS based on the combination of MCP analysis and EDF, together with the error introduced due to the use of the two methodologies. Description: Ph.D.(Melit.) Sun, 01 Jan 2023 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/141615 2023-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/130603 Title: Investigating wind variations within the atmospheric boundary layer : a Maltese case study using LiDAR Abstract: Conventional wind monitoring masts, with sensors at multiple levels, have typically formed an essential part of wind measurement campaigns to gauge wind parameters and climatological behaviour at a potential wind turbine site. As bigger, megawatt-class wind turbines are being introduced into the market, monitoring masts need to be taller to reach the machines’ hub height. However, this results in costlier masts and in particular, makes offshore measurements more challenging. Therefore, remote sensing technologies, such as Light Detecting and Ranging (LiDAR), are becoming increasingly popular due to the precise nature in which they capture wind flow data at altitudes beyond those attainable with traditional monitoring masts. In July 2022 a LiDAR wind measurement system was set up atop the White Tower at l- Aħrax limits of Mellieħa, Malta. Over the course of one calendar year (12 months), spanning from 1 September 2022 to 31 August 2023, data was collected by this LiDAR unit at 10 different heights ranging from 11 m to 191 m above the unit’s window (20 m and 200 m above ground level, respectively). As a result, data collected enabled a more detailed analysis, besides that of wind speed, wind direction, standard deviation of wind speed, turbulence intensity, and temperature. The variation of wind speed shear and wind direction shear were assessed independently and in relation with other collected wind parameters on monthly and cumulative 12-month basis. The objective of this study was to assess the variation of wind within the atmospheric boundary layer at a coastal location in Malta. This study ensued by collecting raw data at the coastal location namely wind speed, wind direction, turbulence intensity, and atmospheric temperature. Whilst the raw data was assessed, their variations, namely wind speed shear and wind direction shear also underwent analysis. The horizontal wind speed at the White Tower measurement was observed to increase with elevation with an overall average of 6.33 ms-1 at 100 m. The diurnal average horizontal wind speed pattern shows that this is low during the night and higher during the day. Furthermore, the vector average wind direction varied from 280.09° (west) at 20 m to 290.73° (west-northwest) at 200 m. In addition, the vector average wind direction at 100 m at the White Tower site was 288.07° (west-northwest). The wind shear exponent yielded was 0.0468 which is significantly lower than the 1/7 (0.14) value generally adopted by the power law. Moreover, the average wind shear exponent increased in magnitude as lower heights were eliminated from the wind shear value derived using the power with values being in line with that observed in literature. Furthermore, the diurnal pattern revealed that the shear exponent decreases during the daytime and increases at nighttime. On the other hand, the variation of the average shear exponent during warm months is observed to be higher during the night whilst negative shear exponent values were noted during daytime. Monthly variations of the average wind direction shear showed no distinct pattern in the difference between the pairs of heights assessed i.e., 200 m & 20 m, 200 m & 100 m, and 100 m & 20 m respective pairs. However, the diurnal variation of the average change in wind direction fluctuates during the warm months, whereas a relatively constant average change in wind direction with minor fluctuations is observed during the cool months. Average wind direction changes are also greater during warm months than those observed during cool months. The turbulence intensity is noted to decrease with higher elevation ranging from 13.1% at 20 m to 9.2% at 200 m. Moreover, the diurnal variation in average turbulence intensity demonstrates fluctuations over the 24-hour period, with higher values during the day and lower values during the night. At the 100 m measurement height, turbulence intensity was highest at low wind speeds and decreased as wind speed increases. Description: M.Sc.(Melit.) Sust.Energy Mon, 01 Jan 2024 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/130603 2024-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/130602 Title: Assessing the impact of energy efficiency measures on building performance and resident behaviour : a case study of social & private housing in Malta Abstract: Energy efficiency behaviour is a complex matter which is related to the interaction between human behaviour and energy consumption. Understanding residents' behaviour could help to mitigate emissions from Malta's building sector, which has witnessed a rise in greenhouse gas (GHG) emissions over the past two decades. The aim of this dissertation is to analyse the energy building performance as well as the behaviour of residents within Malta’s first energy efficient social housing project, the Tal-Ftieh complex. Additionally, a sample of beneficiaries from the Irrinova Darek and Regulator for Energy and Water Services (REWS) Roof thermal insulation and double-glazing financial support initiatives were also included in the study. This research involved a mixed-method approach, which included interviews, temperature and humidity monitoring within the dwellings using data loggers, collection of data from energy bills, and energy performance calculation utilising Design Builder software specifically for the Tal-Ftieh housing project. The results from the DesignBuilder software indicate that the Tal-Ftieh housing apartments are energy efficient as their energy performance is lower than the national average. However, the data collected from the latest available energy bills shows relatively higher energy consumption and also notable variation amongst the participants. Moreover, their daily per capita energy consumption also tends to exceed that of participants from both the REWS and Irrinova Darek schemes. This highlights the important role of residents’ energy behaviour. As regards thermal comfort, the measured indoor temperature of the Tal-Ftieh participants exceeded the international guidelines on several occasions. Furthermore, the measured indoor humidity levels were also high for all participants most of the time, especially for the participants from the REWS scheme, who reside in terraced houses. Meanwhile, the results from the interviews showed that the tal-Ftieh participants had poor knowledge regarding the energy efficient measures installed in the building and this affected their energy behaviour. This scenario contrasts with the REWS and Irrinova Darek beneficiaries for whom the energy efficiency measures constituted an investment risk. Another important finding from the research was that the primary motivator for investing in energy efficient measures is cost savings. Based on these findings, the dissertation presents a number of policy recommendations to enhance energy efficiency behaviour in Malta and make progress towards reducing emissions from the residential building sector. Description: M.Sc.(Melit.) Sust.Energy Mon, 01 Jan 2024 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/130602 2024-01-01T00:00:00Z