This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

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Online Short-term Solar Power Forecasting

A novel wavenets long short term memory paradigm for wind power prediction

Cardio Vascular Disease (CVD) is, for the most part, alluding to conditions that include limited or blocked veins that can prompt a heart attack, chest torment (angina) or stroke. The machine learning classifier predicts the ailment dependent on the state of the side effect endured by the patient. This paper intends to look at the presentation of the Machine learning tree classifiers in anticipating Cardio Vascular Disease (CVD). Machine learning tree classifiers, for example, Random Forest, Decision Tree, Logistic Regression, Support vector machine (SVM), K-nearest neighbors (KNN) were broke down dependent on their precision and AUC ROC scores. In this investigation of foreseeing Cardiovascular Disease, the Random woodland Machine learning classifier accomplished a higher precision of 85%, ROC AUC score of 0.8675 and execution time of 1.09 sec.

Analysis and Prediction of Cardio Vascular Disease using Machine Learning Classifiers

In this article, a hybrid strategy is proposed to track that maximum power of Wind Energy Conversion System (WECS). The proposed technique is the joint execution of Dynamic Differential Annealed Optimization (DDAO) and Recalling Enhanced Recurrent Neural Network (RERNN) hence, it is called D2AORERN2 approach. In the proposed work, DDAO has input parameters, like rectifier outputs that means rectifier dc voltage, dc current, time. Based on input parameters, Dynamic Differential Annealed Optimization optimizes and minimizes the error in rectifier power and makes training dataset based on maximal power point tracking conditions. Based on accomplished dataset, the RERNN finds the optimal solution. The rectifier’s reference dc voltage is transformed for controlling the inverter switch pulses. Finally, the proposed method is activated in MATLAB/Simulink, its superiority is analyzed with various existing methods, like genetic algorithm (GA), particle swarm optimization (PSO), Hill Climb Search (HCS).The efficiency of proposed system is likened to several existing technique as GA, PSO and HCS. The efficiency of GA, PSO, HCS and proposed technique is 81%, 85%, 89% and99%. Thus the proposed technique achieves best result than the other techniques. 

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Leveraging a Dynamic Differential Annealed Optimization and Recalling Enhanced Recurrent Neural Network for Maximum Power Point Tracking in Wind Energy Conversion System

A comparative study on machine learning based heart disease prediction

Heart diseases are the chief cause of death all over the world over the last few decades. To avoid heart disease or coronary illness and discover indications early, individuals over 55 years must have a total cardiovascular checkup. Researchers and specialists developed various intelligent techniques to improve capacity of the health care professionals in recognition of cardiovascular disease. In cardiovascular disease finding and treatment, single data mining strategies are giving the reasonable precision and accuracy. Nevertheless the usage data mining procedure be capable of reducing the number of test that is required to be carried out. In order to decrease the Figure of deaths from heart diseases there has to be a quick and efficient detection technique providing better accuracy and precision. The aim of this paper is to present an efficient technique of predicting heart diseases using machine learning approaches. Hence we proposed a hybrid approach for heart prediction using Random forest classifier and simple k-means algorithm machine learning techniques. The dataset is also evaluated using two other different machine learning algorithms, namely, J48 tree classifier and Naive Bayes classifier and results are compared. Results attained through Random forest classifier and the corresponding confusion matrix shows robustness of the methodology.

A Hybrid Machine Learning Approach for Prediction of Heart Diseases

The forecast of electricity demand in recent years is becoming increasingly relevant because of market deregulation and the introduction of renewable resources. To meet the emerging challenges, advanced intelligent models are built to ensure precise power forecasts for multi-time horizons. The use of intelligent forecasting algorithms is a key feature of smart grids and an effective tool of resolving uncertainty for better cost and energy efficiency decisions like scheduling the generations, reliability and power optimization of the system, and economic smart grid operations. However, prediction accuracy in forecasting algorithms is highly demanded since many important activities of power operators like load dispatch depend upon the short-term forecast. This paper proposes a model for the estimation of the consumption of electricity in Agartala, Tripura in India, which can accurately predict the next 24 h of load with and estimation of load for 1 week to 1 month. A number of specific characteristics in the city have been analysed in order to extract variables that could affect the pattern of electricity consumption directly. In addition, the present paper shows the way to significantly improve the accuracy of the prediction through ensemble machine learning process. We demonstrated the performance of individual Random forest and XGBoost along with their ensemble. The RF and XGBoost ensemble obtained an accuracy with an improvement of 15–29%. The analyses or findings also provide interesting results in connection with energy consumption.

Prediction of electrical energy consumption based on machine learning technique

Wind power generation probabilistic modeling using ensemble learning techniques

3D segmentation of liver and its lesions using optimized geometric contours

Deep neural networks (DNN) oblige large preprocessed samples of training annotated images for successful training, which makes the approach costly particularly in the biomedical imaging domain. The...

Ankur Biswas

Papers

A Hybrid Machine Learning Approach for Prediction of Heart Diseases

Prediction of electrical energy consumption based on machine learning technique

Wind power generation probabilistic modeling using ensemble learning techniques

3D segmentation of liver and its lesions using optimized geometric contours

Data Augmentation for Improved Brain Tumor Segmentation