Predicting Industrial Sectors Energy Consumption: Application of Support Vector Machine To follow in Europeu0027s footstep in minimizing emission rate, much concentration should be on Africau0027s industrial energy consumption. South Africau0027s industrial sector is contributing immensely to the countryu0027s economic growth of which energy plays significant role. To reduce its consumption will mean planning accurately. Forecasting techniques have gained grounds when it comes to planning accessibility to energy demand to prevent incessant increase in emission rate. These techniques include traditional and machine learning techniques. This study applied support vector machine (SVM) of machine learning technique compared to regression analysis of traditional technique. The SVM is applied to forecast South Africau0027s energy consumption of five subsectors (manufacturing, basic nonferrous metals, basic iron and steel, nonmetallic minerals and basic chemicals), with activity, structure and intensity as inputs whereas energy consumed was the output from 1970 to 2016. In contrast to the traditional technique, results confirmed SVM to be a better modelling system in terms of visual inspection (Figures 2 and 3) and statistical measures of performance in Table 1 (correlation coefficient, RMSE and RRSE).

RealTime Power Consumption Monitoring and Forecasting Using Regression Techniques and Machine Learning Algorithms The demand for electricity in the Philippines has been steadily increasing with about onethird of the share going to the residential sector. Thus, there is a need to introduce energy management tools for residences to allow households to take control of their electricity consumption. This work presents a system which provides information on the power consumption of a residence through energy monitoring and forecasting. The system was deployed in a residential unit with a solar PV array and the electricity consumption was monitored for 28 days using an online cloudserver database. Moreover, different regression techniques and machine learning algorithms, such as linear and polynomial regression, support vector regression (SVR) and Random Forest, were trained and implemented to identify the model that gives the best accuracy in predicting the total electricity consumption of the residence at the end of the month. Results show that the linear and polynomial regressions produced large errors due to the nonlinear trend of the consumption data, which is attributed to the generated energy of the solar PV array. The support vector regression algorithm generated models with low percent errors in predicting the end of the month electricity consumption. Moreover, the random forest regression accurately predicted the nextday electricity consumption at 0.58 % error. However, the models generated using Random Forest are not suitable for longterm prediction.