Machine learning applications for photovoltaic system optimization in zero green energy buildings

Liu, Wei; Shen, Yanchang; Aungkulanon, Pasura; Ghalandari, Mohammad; Le, Binh Nguyen; Meza, Aníbal; Cárdenas-Escrocia, Yulineth

doi:10.1016/j.egyr.2023.01.114

Cited by 11 publications

(4 citation statements)

References 43 publications

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“…By analyzing data on wind patterns, climate conditions, and energy usage, AI can maximize energy output while reducing waste. For instance, AI has been used to improve the orientation and performance of solar panels, increasing their energy efficiency [16].…”

Section: Potential Benefits Of Ai In Addressing Climate Changementioning

confidence: 99%

Harnessing the Power of Artificial Intelligence in Climate Change Mitigation: Opportunities and Challenges for Public Health

2024

J Curr Trends Comp Sci Res

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Artificial intelligence (AI) has emerged as a powerful tool for addressing the challenges posed by climate change and their impact on public health. By leveraging its capacity to analyze and predict climatic patterns, AI offers opportunities to enhance resource management and develop effective strategies for climate change mitigation. Moreover, AI can contribute to generating sustainable solutions that address the complex and interconnected nature of climate change. For example, AI can enable the optimization of energy consumption and facilitate the integration of renewable energy sources into existing systems. It can also support the development of climate models that provide timely and accurate predictions, enabling policymakers to implement proactive measures for disaster preparedness and response. Furthermore, AI-powered disease surveillance and mitigation techniques can improve public health outcomes by identifying patterns and trends in the spread of diseases in relation to climatic factors. However, the widespread adoption of AI-based solutions is not without challenges. Ethical concerns surrounding privacy and data ownership must be addressed, as the use of AI requires access to large datasets, raising potential privacy risks. Technical constraints, such as limited computational power and the need for sophisticated algorithms, also pose obstacles to the implementation of AI in climate change mitigation strategies. Furthermore, issues related to the accessibility and affordability of AI technologies must be resolved to ensure equitable distribution and maximize its potential impact on public health. To fully harness the power of AI in addressing climate change and improving public health outcomes, it is crucial to promote innovation, multidisciplinary collaboration, and open data science. Innovation can drive the development of new AI algorithms and technologies specifically tailored to address climate change challenges. Multidisciplinary approaches that bring together experts from diverse fields, including climate science, public health, and computer science, can foster a holistic understanding of complex systems and enable the design of comprehensive solutions. Finally, open data science practices, such as sharing data and algorithms, can facilitate collaboration and accelerate progress in mitigating climate change and its public health impacts. In conclusion, AI offers promising opportunities for effectively addressing climate change and its impact on public health. However, to fully realize its potential, it is essential to tackle ethical and privacy concerns, overcome technical constraints, and ensure accessibility and affordability. By promoting innovation, multidisciplinary collaboration, and open data science, we can unlock the transformative power of AI in mitigating climate change and improving public health outcomes.

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Section: Potential Benefits Of Ai In Addressing Climate Changementioning

confidence: 99%

Harnessing the Power of Artificial Intelligence in Climate Change Mitigation: Opportunities and Challenges for Public Health

2024

J Curr Trends Comp Sci Res

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“…Support Vector Machine (SVM) is a supervised learning model that finds the hyperplane that best separates different classes by maximizing the margin between the closest points of the classes (Liu et al, 2023). For linear SVM, the separating hyperplane is given by the equation 5.…”

Section: Second Step -Models Trainingmentioning

confidence: 99%

Tracing Andean Origins: A Machine Learning Framework for Lead Isotopes

Alviz-Meza

2024

Preprint

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This study uses machine learning techniques to facilitate the geolocation of Andean lead isotopes, a novel approach in this geographical context. Two predictive models for latitude and longitude were developed based on the compilation of a database of the lead isotope ratios 206Pb/204Pb, 207Pb/204Pb, and 208Pb/204Pb from multiple Andean provinces. These models were cross-validated using GridsearchCV to assess their performance, identifying Random Forest as the best-performing model. Also, clustering analysis with the K-means model and Euclidean distance was used to correlate artifact isotope compositions with known sources. The limitations and scope of the models were listed for their appropriate usability and interpretability. This work extends basic geochronological studies, integrates a comprehensive database, and applies state-of-the-art algorithms to generate predictive models, contributing to a deeper understanding of the historical Andean mineral resources' distribution and use.

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“…Based on that, the researchers focused on proposing different algorithms to guarantee accurate and fast tracking to the Maximum Power Point (MPP). Regardless of the type of PV system, whether it is a standalone or grid-connected system, guaranteeing MPP tracking condition maximizes the level of harvested energy from the incident sunlight [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. The proposed algorithms have different characteristics, including both pros and cons, in terms of response speed, tracking accuracy, and oscillation at steady state conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Artificial Intelligence (AI) represented by ANN algorithm is a machine learning algorithm, which works by manipulating the data of input variables to have an accurate prediction for the missed values of the required variables. ANN is playing an effective role for the objective of guaranteeing MPPT in PV system applications by controlling different types of DC-DC converters [16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Efficient Photovoltaic Unit for Power Delivering to Stand-Alone Direct Current Buildings Using Artificial Intelligence Approach Based MPP Tracker

Attia,

Delama

2023

Sustainability

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There are many remote buildings that cannot be supplied by alternating electricity of the utility grid. Due to this, this study proposes adopting Direct Current (DC) appliances for a stand-alone remote building. Direct Current can be supplied from a suitable photovoltaic array which can harvest renewable solar energy. This proposal guarantees an efficient power system by removing the necessity of including an inverter, power filter, insulation transformer, and a complicated controller, which are usually needed for producing Alternating Current (AC) power to feed AC loads using a PV system. When the proposal is applied, the PV system will be more efficient, simple, affordable, and more compact. A detailed power requirement calculation for a typical house uses DC appliances, generalized steps to design a suitable PV array, and an Artificial Neural Network (ANN) algorithm for guaranteeing Maximum Power Point Tracking (MPPT); all of which are introduced for remote buildings. The main contribution of this paper is proposing an integrated design of a DC unit of 11 kW·h PV system for stand-alone buildings that eliminates three stages that improves the system performance compared to AC unit. The introduced study includes PV array calculation based on PV module of 220 W with an intelligent algorithm of four layers. The Mean Squared Error (MSE) of the proposed ANN equals 2.7107 × 10−5 to guarantee a fast and accurate MPP tracking for continuously harvesting maximum power from the incident sunlight. An energy storage unit of 12 batteries 12 V/150 Ah of matrix dimensions 3 × 4 is designed in the DC unit for energy saving to feed the DC appliances during night hours. MATLAB/Simulink Version R2015b is used to simulate the introduced DC power unit and collect the testing records for analyzing the unit performance.

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Machine learning applications for photovoltaic system optimization in zero green energy buildings

Cited by 11 publications

References 43 publications

Harnessing the Power of Artificial Intelligence in Climate Change Mitigation: Opportunities and Challenges for Public Health

Harnessing the Power of Artificial Intelligence in Climate Change Mitigation: Opportunities and Challenges for Public Health

Tracing Andean Origins: A Machine Learning Framework for Lead Isotopes

Efficient Photovoltaic Unit for Power Delivering to Stand-Alone Direct Current Buildings Using Artificial Intelligence Approach Based MPP Tracker

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