TITLE:
Enhancing Urban Intelligence Energy Management: Innovative Load Forecasting Techniques for Electrical Networks
AUTHORS:
Zeinab Farrokhi, Kamran Hassanpouri Baesmat, Emma E. Regentova
KEYWORDS:
Energy Prediction, Forecasting, Regression, Neural Network, MTLF
JOURNAL NAME:
Journal of Power and Energy Engineering,
Vol.12 No.11,
November
26,
2024
ABSTRACT: Energy sustains the world, yet fossil fuels, a finite resource, are dwindling. This necessitates a shift towards more sustainable energy sources, such as electricity. Accurate load forecasting is crucial in today’s global energy landscape, as it helps predict various aspects such as production, revenue, consumption, economic conditions, weather impacts, power system utilization, customer demand, and economic growth. For instance, an increase in electricity demand within a country often signifies a boost in industry and production, leading to economic progress and reduced unemployment. This project aims to enhance prediction accuracy through meticulous input filtering, taking into account factors like population growth, planned loads, inflation, and competitive pricing pressures from producers. Despite inherent prediction errors, efforts are made to minimize these discrepancies. This paper introduces a novel combined method for mid-term energy forecasting. To demonstrate its efficacy, real data from the past ten months, collected from subscribers of the Kerman distribution company, was used to forecast energy consumption over the next ten days. The innovative method, which integrates multiple forecasting techniques and robust filters, significantly improves forecasting precision. The following error metrics were recorded for the proposed method: MSE: 0.009, MAE: 0.083, MAPE: 0.776, RMSE: 0.095, AE: 0.013.