Hello All! I created this webpage to spread awareness about the latest innovations in renewable energy and the impacts of traditional methods. My goal is to get more people involved with utilizing these new technologies to the fullest extent to create a better ecological environment. To all homeowners interested in renewable energy, but not aware of the latest innovations in the field, I present to you techniques that will make your systems more efficient and effective.
The Agenda
Machine Learning
What is it?
Machine learning is a tricky concept. If what I'm about to say doesn't make much sense, hopefully the video above can further assist you. Machine learning is a method of artificial intelligence for teaching computers to learn from data without being explicitly programmed.
The process is as follows:
- Data collection
- Analysis of the data
- Creation of desired outputs
I, for one, had a hard time fully understanding the concept of machine learning because I thought too hard about how it worked. In reality, it's a piece of cake! To help explain the concept to a general audience, I had my good friend, Peter, give me an analogy for it.
- Machine learning can be thought of as a recipe for a cake.
- Just as a recipe tells you how to mix and cook ingredients to achieve a desired flavor and texture, a machine learning algorithm is designed to learn from data and identify patterns to achieve a desired outcome
Machine Learning in Solar Energy
Where Machine Learning Fits
Machine learning for solar energy requires specific pieces of data:
- The energy consumption of current PV systems
- solar panel efficiency
- weather patterns of the region to find out when times of solar generation are rampant.
- After being trained with a given set of these inputs, the design of a machine learning algorithm can calculate which region or time of day can provide the most energy.
Why Does Machine Learning Help?
According to Almadhor et al. (2022), forecasting power production using weather data has several financial advantages:
- dependable proactive power trading
- operation planning
- reduced maintenance costs
This research uses linear regression techniques to forecast utilizing artificial neural networks (ANN). ANNs showed high accuracy in forecasting energy demand in cities based on past initiatives. The results of this study demonstrate 99.9% reliability rate for ANN model prediction of the electrical power usage during the summer and winter seasons.
Incorporating Solar into Architecture
Architecturally Suitable PV Grid System
Looking into other innovations, Lomi et al (2022) explore architecturally suitable PV grid systems.
- This basically involves planning and strategies which integrate PV systems into building envelopes to generate electricity
- This is a solar energy system that is designed to fit nicely on top of a building's roof or on the ground nearby, and that is set up to capture as much sunlight as possible in order to create electricity
- In other words, there are either stand-alone renewables or those that work in integration with the building's main power grid.
Stand-Alone vs Integrated
Integrating solar panels into a building's electrical grid system has many more benefits than using them as standalone power generators. For Example:
- Reduced electricity bills
- Increased reliability
- Environmental benefits
- Higher resale value
When solar panels are integrated into a building's grid system, any excess power generated can be exported to the grid for others to use.
Nanofluid-based Cooling
At this point, I know what you must be thinking: What is nanofluid-based cooling supposed to mean? Well, this is just an addition to common ways of cooling solar panels.
- The panels require cooling because they overheat from prolonged periods of concentrated solar energy
- Jose et al. (2023) present a new technology that uses nanoparticles to make heat travel slower(presented in the figure to the right)
- The technology was fitted under the solar panel absorber plate(which can be visualized in F.2) with a pure water and aluminum oxide base fluid(F.1).
- Normally, the fluid to cool PV cells is just water that runs through the serpentine copper tubes
- Since nanoparticles make the fluid so dense, it causes the system to move much slower
- This helped the system stay much cooler, increasing energy performance by 20%.
Urban Landscape Design
Why Care?
- In 2020, about 79% of energy consumption in the U.S. came from fossil fuels, primarily natural gas, coal, and oil
- With concern for future generations, it is imperative that more efforts are done to prepare for climate change
- Instead of improvising renewable energy methods in urban landscapes, new smart cities need to practice proper planning and provide a much more efficient system of electricity generation.
How Bad are Traditional Methods?
- Daily carbon emissions from traditional materials are twice as high as those from solar energy as Wu et al. (2022) say
- The graph below shows the major difference in emissions from each electricity source.
- Utilizing these new innovations will not be easy without proper urban landscape planning and design
- This means accurately pinpointing locations of optimal power generation by fitting solar panels in the best spots
- What this gains for a community is efficient use of space, sustainability, social cohesion, and overall improved quality of life.
Gaps in the research
Limitations of Solar Panels
- The scope of PV designs could be too tight, many designs of solar panels have to be taken into consideration
- certain architectural designs in developing countries have limited applicability
- Additionally, more work needs to be done on improving the cooling tube layout and nanofluid concentrations
External Conflicts
- Lots of unpredictability to take into account: Climate Change
- In the future, in-depth discussions with researchers will be made to make certain contributions to the prediction
- Environmental and socioeconomic factors across different regions and countries may not be able to be considered
- Inadequate validation of data, meaning some studies may not provide sufficient validation of the methods and models.
To prepare for the future of climate change, the concepts described in my research need to be used in a multidisciplinary approach. As these fields continue to evolve, it is imperative to continue utilizing these new techniques to prepare the future for climate change.
Thank you!