Artificial Intelligence Forecasting Techniques For Reducing Uncertainties In Renewable Energy Applications [védés előtt]

The work presented in this thesis provides an integrated view and related insights for solar and wind farm operators and renewable energy regulators regarding factors influencing electricity production using those resources. The findings help production planning and grid stability improvements through better energy forecasting to reduce uncertainty. With the high increase in energy demand expected in both the near and far future, generating energy from green sustainable resources has now become an imperative necessity. Renewable energy sources like wind and solar are among the most promising and environmentally-friendly energy generation options. However, wind and solar energy production are influenced by many variables which affect the reliability, stability, and economic benefits of wind and solar energy projects, therefore, forecasting the potential amount of energy from wind and solar resources is of great importance. Hence, the objective of the work reported here was to explore the possibility of using artificial intelligence methods to accurately predict the generated renewable power from solar and wind farms based on the available data. Specifically, this thesis reports on the following results: 1. At first, solar photovoltaic (PV) energy forecasting was studied. Operators of grid-connected PV farms do not always have full sets of data available to them, especially over an extended period of time as required by key forecasting techniques such as multiple regression (MR) or artificial neural network (ANN). Therefore, the work reported here considered these two main approaches of building prediction models and compared their performance when utilizing structural, time-series, and hybrid methods for data input. Three years of PV power generation data (of an actual farm), as well as historical weather data (of the same location) with several key variables, were collected and utilized to build and test six prediction models. Models were built and designed to forecast the PV power for a 24-hour ahead horizon with 15 minutes resolutions. Results of comparative performance analysis show that different models have different prediction accuracy depending on the input method used to build the model: ANN models perform better than the MR regardless of the input method used. The hybrid input method results in better prediction accuracy for both MR and ANN techniques while using the time-series method results in the least accurate forecasting models. Furthermore, sensitivity analysis shows that poor data quality does impact forecasting accuracy negatively, especially for the structural approach. 2. Then, wind energy forecasting was studied utilizing three machine learning techniques namely Artificial Neural Network (ANN), Support vector machine (SVM), and k-nearest neighbors (K-NN). The three mentioned techniques were used to design, train and test wind energy, forecasting models. Later, a hybrid model was proposed based on these three techniques. Real data obtained from a 2MW grid-connected wind turbine has been used to train and validate the different machine learning techniques. To compare the accuracy of the models over different performance measures with different scales, a comparative evaluation method was devised and used. Results show that the ANN model has great performance in forecasting long-term wind energy, but in contrast, it has very poor short-term performance. SVM model shows better short-term forecasting performance than ANN but presents weak long-term forecasting abilities. K-NN model shows very good short-term forecasting abilities and fair long-term performance. The suggested hybrid model was able to forecast both long and short-term wind energy with very good performance. To that degree, the suggested model can help grid and wind farm operators to forecast the potential amount of wind energy for both long and short term with a good degree of certainty. 3. Finally, the effect of the input data resolution on the forecasting accuracy was studied for both wind and solar. So, datasets were collected from a 546 KWp grid-connected PV farm and a 2 MW wind turbine for one full year. This data was used to train and test Artificial Neural Network models to forecast day-ahead PV and wind energy utilizing time-series input data with 15, 30, and 60 minutes resolutions. The models were able to forecast the PV energy accurately, while the same models trained for wind showed poor performance. Higher input data resolutions lead to slightly better forecasting performance for the PV farm. Utilizing data with higher resolution can improve the forecast by 1-5%. While for wind energy forecasting, the resolution has very minor effects, the 30-minute resolution shows a bit better forecasting performance

Implementing decision tree-based algorithms in medical diagnostic decision support systems

As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems. Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks. We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospital’s dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models

An agent-based approach to model farmers' land use cover change intentions

Land Use and Cover Change (LUCC) occurs as a consequence of both natural and human activities, causing impacts on biophysical and agricultural resources. In enlarged urban regions, the major changes are those that occur from agriculture to urban uses. Urban uses compete with rural ones due among others, to population growth and housing demand. This competition and the rapid nature of change can lead to fragmented and scattered land use development generating new challenges, for example, concerning food security, soil and biodiversity preservation, among others. Landowners play a key role in LUCC. In peri-urban contexts, three interrelated key actors are pre-eminent in LUCC complex process: 1) investors or developers, who are waiting to take advantage of urban development to obtain the highest profit margin. They rely on population growth, housing demand and spatial planning strategies; 2) farmers, who are affected by urban development and intend to capitalise on their investment, or farmers who own property for amenity and lifestyle values; 3) and at a broader scale, land use planners/ decision-makers. Farmers’ participation in the real estate market as buyers, sellers or developers and in the land renting market has major implications for LUCC because they have the capacity for financial investment and to control future agricultural land use. Several studies have analysed farmer decision-making processes in peri-urban regions. These studies identified agricultural areas as the most vulnerable to changes, and where farmers are presented with the choice of maintaining their agricultural activities and maximising the production potential of their crops or selling their farmland to land investors. Also, some evaluate the behavioural response of peri-urban farmers to urban development, and income from agricultural production, agritourism, and off-farm employment. Uncertainty about future land profits is a major motivator for decisions to transform farmland into urban development. Thus, LUCC occurs when the value of expected urban development rents exceeds the value of agricultural ones. Some studies have considered two main approaches in analysing farmer decisions: how drivers influence farmer’s decisions; and how their decisions influence LUCC. To analyse farmers’ decisions is to acknowledge the present and future trends and their potential spatial impacts. Simulation models, using cellular automata (CA), artificial neural networks (ANN) or agent-based systems (ABM) are commonly used. This PhD research aims to propose a model to understand the agricultural land-use change in a peri-urban context. We seek to understand how human drivers (e.g., demographic, economic, planning) and biophysical drivers can affect farmer’s intentions regarding the future agricultural land and model those intentions. This study presents an exploratory analysis aimed at understanding the complex dynamics of LUCC based on farmers’ intentions when they are faced with four scenarios with the time horizon of 2025: the A0 scenario – based on current demographic, social and economic trends and investigating what happens if conditions are maintained (BAU); the A1 scenario – based on a regional food security; the A2 scenario – based on climate change; and the B0 scenario – based on farming under urban pressure, and investigating what happens if people start to move to rural areas. These scenarios were selected because of the early urbanisation of the study area, as a consequence of economic, social and demographic development; and because of the interest in preserving and maintaining agriculture as an essential resource. Also, Torres Vedras represents one of the leading suppliers of agricultural goods (mainly fresh fruits, vegetables, and wine) in Portugal. To model LUCC a CA-Markov, an ANN-multilayer perceptron, and an ABM approach were applied. Our results suggest that significant LUCC will occur depending on farmers’ intentions in different scenarios. The highlights are: (1) the highest growth in permanently irrigated land in the A1 scenario; (2) the most significant drop in non-irrigated arable land, and the highest growth in the forest and semi-natural areas in the A2 scenario; and (3) the greatest urban growth was recognised in the B0 scenario. To verify if the fitting simulations performed well, statistical analysis to measure agreement and quantity-allocation disagreements and a participatory workshop with local stakeholders to validate the achieved results were applied. These outcomes could provide decision-makers with the capacity to observe different possible futures in ‘what if’ scenarios, allowing them to anticipate future uncertainties, and consequently allowing them the possibility to choose the more desirable future