Indoor smart farming by inducing artificial climate for high value-added crops in optimal duration

The global population is increasing rapidly as compared to food production;approximately three times more food would be required in 2050.Climate change affects crop production by causing sudden changes in weather conditions,including rain,storms,heat waves,doughiness,and water shortages.Farming with smart technology provides a productive solution.Smart farming is a productive solution that provides a great resource of income and improves the countries’economy by exporting consumable goods and preventing food security problems.Smart agriculture provides a combination of flexibility,remote access,and automation through the use of intelligent control technologies.Many countries are working towards smart and intelligent agriculture farming that analyzes crop,soil fertility,pests and weeds,and other problems caused by mismanagement and incompetence.However,smart agricultural farming is less widely adopted in agriculture as a result of high costs and little understanding of technology.In this study,An artificial climate control chamber(ACCC)was designed for cultivating plants by controlling the optimal parameters,especially the light spectrum.In ACCC,influential plant factors such as light,moisture,humidity,and fertilizer concentration have been controlled intelligently.Light spectrum was controlled by time periods in the previous system,while in the system proposed in this study,the light was controlled by image processing.In an artificial control chamber,the plant growth stages have been determined through image processing techniques.Datasets of image images have been used to organize specific intensities of the light spectrum.This intelligent system provides aid in the speed breeding procedure through variant spectrums of light and fertilizers combinations.In the research study,the yield and quality of intelligent farming are enhanced.

Comparative Evaluation of Machine Learning Models and Input Feature Space for Non-intrusive Load Monitoring

Recent advancement in computational capabilities has accelerated the research and development of non-intrusive load disaggregation.Non-intrusive load monitoring(NILM)offers many promising applications in the context of energy efficiency and conservation.Load classification is a key component of NILM that relies on different artificial intelligence techniques,e.g.,machine learning.This study employs different machine learning models for load classification and presents a comprehensive performance evaluation of the employed models along with their comparative analysis.Moreover,this study also analyzes the role of input feature space dimensionality in the context of classification performance.For the above purposes,an event-based NILM methodology is presented and comprehensive digital simulation studies are carried out on a low sampling real-world electricity load acquired from four different households.Based on the presented analysis,it is concluded that the presented methodology yields promising results and the employed machine learning models generalize well for the invisible diverse testing data.The multi-layer perceptron learning model based on the neural network approach emerges as the most promising classifier.Furthermore,it is also noted that it significantly facilitates the classification performance by reducing the input feature space dimensionality.

Non-invasive load-shed authentication model for demand response applications assisted by event-based non-intrusive load monitoring

With today’s growth of prosumers and renewable energy resources,it is inevitable to incorporate the demand-side approaches for reliable and sustainable grid operation.In this context,demand response is a promising technique facilitating the consumers to play a substantial role in the energy market by altering their energy consumption patterns in times of peak demand or other critical contingencies.However,effective demand response deployment faces numerous challenges including trust deficit among the concerned stakeholders.This paper addresses the mentioned issue by proposing a non-invasive load-shed authentication model for demand response applications,assisted by an improved event-based non-intrusive load monitoring approach.For the said purposes,an improved event detection algorithm and machine learning model:support vector machine with a combination of genetic algorithm and GridSearchCV,is presented.This paper also presents a comprehensive real-world case study to validate the effectiveness of the proposed model in a real-life scenario.In the given context,all the simulations are carried out on low sampling real-world load measurements:Pecan Street-Dataport,where electric vehicle and air conditioning are employed as potential load elements for evaluation purposes.Based on the presented case study and analysis of the results,it is established that the presented improved event-based non-intrusive load monitoring approach yields promising performance in the context of multi-class classification.Moreover,it is also concluded that the proposed low sampling event-based non-intrusive load monitoring assisted non-invasive load-shed authentication model is a viable and promising solution for the effective implementation of demand response applications.