Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is satisfied. It divides the measured data for actual power (A_p ) of the electrical model into two portions: the training portion is selected for different maximum actual powers, and the validation portion is determined based on the minimum output power consumption and then used for comparison with the actual required input power. Simulation results show the energy expenditure problem can be solved with good accuracy in energy consumption by reducing the maximum rate (A_p ) in a given time (24) hours for a single house, as well as electricity’s bill cost, is reduced.

In this work, an efficient energy management (EEM) approach is proposed to merge IoT technology to enhance electric smart meters by working together to satisfy the best result of the electricity customer's consumption. This proposed system is called an integrated Internet of things for electrical smart meter (2IOT-ESM) architecture. The electric smart meter (ESM) is the first and most important technique used to measure the active power, current, and energy consumption for the house’s loads. At the same time, the effectiveness of this work includes equipping ESM with an additional storage capacity that ensures that the measurements are not lost in the event of a failure or sudden outage in WiFi network. Then then these measurements are sent using the internet of thing (IoT) technology to Google Firebase cloud, where the electric consumer's service center is located to store, analyze the measured data, and detect cases of energy penetration when it exceeds 53  and the cases of the electrical energy theft if any below 20  and then take the appropriate decision about it. Finally, an electric smart metering application (ESM-app) is designed and implemented to read and pull data information from the Google firebase cloud and then send the electric bill to the end consumer, and sending alert messages to the thieves and electrical power hackers to prohibit them if something wrong has detected. In this work, an efficient energy management (EEM) approach is proposed to merge IoT technology to enhance electric smart meters by working together to satisfy the best result of the electricity customer's consumption. This proposed system is called an integrated Internet of things for electrical smart meter (2IOT-ESM) architecture. The electric smart meter (ESM) is the first and most important technique used to measure the active power, current, and energy consumption for the house’s loads. At the same time, the effectiveness of this work includes equipping ESM with an additional storage capacity that ensures that the measurements are not lost in the event of a failure or sudden outage in WiFi network. Then then these measurements are sent using the internet of thing (IoT) technology to Google Firebase cloud, where the electric consumer's service center is located to store, analyze the measured data, and detect cases of energy penetration when it exceeds 53  and the cases of the electrical energy theft if any below 20  and then take the appropriate decision about it. Finally, an electric smart metering application (ESM-app) is designed and implemented to read and pull data information from the Google firebase cloud and then send the electric bill to the end consumer, and sending alert messages to the thieves and electrical power hackers to prohibit them if something wrong has detected.