Application of Short-Term Load Forecasting for Optimizing the Storage Devices of a Base Station
Energy is one of the important key factors to realize better socioeconomic development of a society and electrical energy is the most common form of energy for urban area both in commercials and residences. The instantaneous nature of electricity has made it different from other commodities as it has to be consumed just after the moment of generation. So, from generation parties to consumers at every stage of modern electricity grid it is every important to ensure the balance of consumption and production to achieve sustainability and reliability of the grid. Load forecasting is an important component for power system energy management system. Precise load forecasting helps the electric utility to make unit commitment decisions, reduces spinning reserve capacity and schedule device maintenance plan properly. It also reduces the generation cost and increases reliability of power systems. In this work, an artificial neural network for short term load forecasting is demonstrated. Based on the time and similar previous day load, artificial neural network model is built, which are eventually used for the short-term load forecasting. The aim of this work is to describe the development and evaluation of a forecasting model to schedule the onsite storage devices. The evaluated model is able to predict the day-ahead electricity demand of a traditional base unit in order to schedule the storage devices.
