A Network of Neural Model for Small Term Load Prediction Using Novel Feedforward (FITNET): Network of Neural Model - Rehman, Khalid; Malik Altamash; Jan Sher Khan; Junaid Miraj; Zaheer Farooq (Pakistan Journal of Emerging Science and Technologies (PJEST), 2025)
Load forecasting is a challenging task in the setting of modern power systems, which have risen in complexity as conventional and non-conventional energy sources have been integrated into an increasingly varied energy environment. Utility companies are under growing pressure to not just provide cost-effective and adequate power generation, but also to maintain system dependability for today's discriminating customers. While there are several load forecasting systems, neural network-based techniques appear as a potential alternative due to their ability to reveal hidden subtleties within the input/output load data connection, resulting in fewer predicting mistakes. Artificial neural networks (ANNs)-based short-term load prediction methods have become more widely used, successfully overcoming issues related to weather, temperature, humidity, precipitation, air pressure, and the shifting patterns of human and industrial activity. This has made accurate load forecasting easier. We present FITNET, a novel feedforward neural network model designed for short-term load prediction (STLF), as our contribution to this effort. FITNET is Zunique in that it can adjust to events occurring in real time and allows training with a wide range of input kinds and sequences. We collected data from the ISO New England NE-Pool region over a period of four and a half years and combined it into a single, coherent dataset. Important inputs include time-related components and meteorological characteristics, such as day and night, dew point, and dry bulb temperature, with weekdays having a substantial impact on the output data. To improve the performance of the ANN model, we carefully examined alternate neuron configurations, using the Levenberg-Marquardt backpropagation approach for training. Extensive testing of our suggested model across both weekly and daily load forecasting methodologies continually shows outstanding efficiency, with the ANN model constantly having a forecasting MAPE of less than 1%. This finding emphasizes the model's stability and its potential to considerably improve the dependability and cost-effectiveness of power generation in today's complex and ever-changing energy landscape.