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.

Smart meters data for modeling and forecasting water demand at the userlevel Abstract   Smart meters installed at the userlevel provide a new data source for managing water infrastructure. This research explores the use of machine learning methods, including Random Forests (RFs), Artificial Neural Networks (ANNs), and Support Vector Regression (SVR) to forecast hourly water demand at 90 accounts using smartmetered data. Demands are predicted using lagged demand, seasonality, weather, and household characteristics. Timeseries clustering is applied to delineate data based on the time of day and days of the week, which improves model performance. Two modeling approaches are compared. Individual models are developed separately for each meter, and a Group model is trained using a data set of multiple meters. Individual models predict demands at meters in the original data set with lower error than Group models, while the Group model predicts demands at new meters with lower error than Individual models. Results demonstrate that RF and ANN perform better than SVR across all scenarios.