Monitoring of photovoltaic systems for selfconsumption without overconsumption The general objective of this article is to implement a low cost monitoring system for inhouse photovoltaic (PV) systems for selfconsumption but avoiding overconsumption in order to optimize the size and cost, of the batteries and PV modules. The implemented system will not only monitor the general power parameters like voltage or current in the inverter, but also to send data to the cloud in order to potentially generate statistics and, with a related app, will render system values in real time too.Without altering the consume patterns in a traditional home, this project aim is to study how to partially supply with solar energy the consumption in a house. It is also planned how to shift loads from a period commercially known as novalley or peak to a more economic one known as valley, when the energy supplied by PV is insufficient. For this purpose a cost efficient system has been developed and tested.

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.