Energy efficiency strategies based on daylight-artificial light integration have grown exponentially in recent years. Taking into account the dynamics to be considered for control and the dependence on natural and occupancy factors, it is better to use a test workbench prior to setting up the final control scheme. This work describes a climate model based test workbench for the real time testing of the control of luminaires and window blinds in a daylight-artificial light integrated scheme. The established climate model based control scheme suitable for the optimum integration of visual comfort, thermal comfort, and energy consumption can be tested for any ecological conditions. The input irradiance from a BF5 sensor, the internal temperature from a Micro DAQ logger, the occupancy and photo sensors associated with the luminaire all provide input data for the test workbench. A fuzzy logic based motorized window blind controller and look-up table based dimming of LED luminaires are used to set the required illuminance with reduced load on the heating, ventilation, and air conditioning system. The anticipated synergetic effects of the test workbench have been validated using real time climate data. The test work bench is established on a Labview platform and developed as a standalone system using myRIO.
The different thermal comfort indices such as Predictive Mean Vote (PMV), Standard Effective Temperature (SET), and Thermal Sensations (TS) have been used to predict occupants’ thermal comfort in a building. The advances in the machine learning approach help overcome the challenges of predicting current traditional thermal indices in a real-time environment. The different indices have different types of data samples (continuous/labelled). Therefore, while considering the machine learning technique in developing the models of the predictive thermal indices, it is essential to select the vital features, the proper learning type, the algorithm, and the evaluation method to establish the models of the predictive thermal comfort. The main focus of this paper is on the development of the ML model and the evaluation technique that helps in selecting the best model in predicting the thermal indices. This work proposes the new neighbourhood-component-analysis Bayesian-optimization-algorithm-based artificial-neural-network to develop a predictive model for the thermal indices. Here, we have proposed a regression-based model to predict PMV, SET and a classification-based model to predict 7-point TS. The statistical-testing results specify that the ANN model's performance is highly accurate and more reliable in predicting the thermal perception in a real-time environment. The performance of the selected model is validated using subjective measures. This prediction leads to the pre-emptive control of the setpoint temperature of the air-conditioning unit, hence resulting in energy efficiency and comfort.
Buildings consume tremendous energy for the improvement of living and working conditions. Control of daylight-artificial light has the potential to improve energy performance and occupant comfort in buildings. This research proposes an intelligent generalized ensemble learning technique to develop a novel control strategy for Venetian-blind positioning (up-down movement with static slat angle of 45 • ) of different window orientations. The proposed model helps to maintain occupant comfort and energy saving in a commercial building. The performance of the ensemble learning approach compared against Gaussian process regression, support vector regression and artificial neural network using conventional statistical indicators. Finally, the proposed data-driven model implemented in a real-time Labview-myRIO platform for the experimental validation. The data-driven model is compared with the baseline model and with the uncontrolled blind condition in terms of daylight glare, and energy consumption of lighting and air-conditioning system in the building. The data-driven model is derived using two years of data collected from a fuzzy-based daylight-artificial light integrated scheme. The blind position providing reduced energy consumption and daylight glare along with setpoint illuminance and temperature are validated. A high dynamic range image with EVALGLARE software used to verify the visual comfort based on daylight glare probability. While evaluating the overall energy savings, the ensemble learning model consumes 17% less power than the uncontrolled system and 15% less power than the baseline system. Here, though we are not controlling the air-conditioning system, the experimental validation confirmed that the air-conditioning system significantly reduces its energy consumption.INDEX TERMS Window blind control, data-driven models, ensemble learning, bayesian optimization, daylight glare, labview, myRIO, energy comparison, lighting control, air-conditioning.
The use of Credit cards has drastically increased as they become one of the vital and most used modes of payment. Along with this the Credit Card. Fraud has also grown to a greater extent. Thus, it becomes vital for Credit card companies or banks to identify fraudulent transactions. Machine learning algorithms are used to analyze and identify suspicious transactions. Credit Card Fraud detection is a typical case of classification and data analysis, in this, we have focused on analyzing and comparing the data on parameters like accuracy, precision on the basis of two supervised learning classification algorithm Logistics Regression and Decision tree.
The societal lifetime of each individual has created with online social media. These locations have made outrageous improvements in the socialize environment. The world's targetable and fashionable Online Social Network (OSN) is Facebook, and it has brilliantly had more than a billion clients. It is a household to numerous kinds of antagonistic objects who misuse the sites by posting harmful or wrong messages. In few years, Twitter and other blogging sites have been around multimillion energetic users. It converted a novel means of rumor-spreading stage. The problem of detecting rumors is now more important, especially in OSNs. In this paper, we proposed rumor a different machine learning approaches as Naïve Bayes, Decision tree, Deep learning and Random forest algorithm for identifying rumors. The experiment can be done with Rapid miner tool on everyday data from Facebook. The schemes of rumor identification are verified by smearing fifteen sorts based on user's performances in Facebook data set to forecast whether a microblog post is a rumor or not. From the experiments, precision, recall, f-score value is calculated for all the four machine learning algorithms, further values are compared to find the accuracy (%) in all the four algorithms. And our experimental result shows that the overall average of precision for a Random forest provides 97% than the other comparative methods.
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