Prediction of Energy Consumed by Home Appliances with the Visualization of Plot Analysis Applying Different Classification Algorithm

Abstract

This paper mainly studies about the prediction of energy consumed by appliances that have been generally used in the house for low energy. Weather records from an adjacent station were collected in order to develop the forecast of the dataset. Dataset used in this paper contains measurements of temperature besides humidity measuring device from a wireless system, climate from an adjacent airport station, and recorded energy consumption of illumination features. Temperature and humidity try to increase the prediction accuracy. This paper discusses some classifiers to predict the humidity and classify the data according to the attributes. Five classification representations were trained with reiterated cross-validation and calculated in the dataset: (a) KNN, (b) support vector machine, (c) SGD, (d) random forest, and (e) neural network. In this paper, sieve diagram has been introduced for comparing the classifiers with one another which is a crucial contribution to our paper. Python base orange data mining tools have been used in this paper. In simulation result, box plot represents standard deviation (SD) and median and epitomizes the values between the first quartile and the third quartile. The Sieve diagram describes the difference between observed and expected frequency whrere it looks like as the density of covering whether the deviation or divergence from independence is negative (red) or positive (blue). Scatter plot shows regression line and regression value which is compared with each other.