Energy forecasting plays a vital role in the proper planning and operation of a grid that has renewables integrated into the conventional grid. To optimize the energy resources, the consumers and the grid operators must know the amount of energy that will be produced over the next few hours or days so that the load prioritization can be planned accordingly. Load prioritizing plays a critical role in maintaining grid stability and ensuring a reliable and sustainable energy supply. This paper presents a cost-effective intelligent system for load forecasting and prioritizing using the PV WATT algorithm to provide day-ahead forecasts in an Internet of Things (IoT) environment. The root mean squared error (RMSE) is 2.22 using this algorithm, underscoring the effectiveness of the implemented system. Prototype implementation involves a cost-effective solar rooftop energy management system along with a 10-W solar panel, a 12 V lead-acid battery, and an Arduino Uno microcontroller. The system's standout feature is that it is a direct current (DC)-based grid, which minimizes the conversion complexities and costs.