Abstract
Electricity demand forecasting plays an important role in capacity planning, scheduling, and the operation of power systems. Reliable and accurate prediction of electricity demands is therefore vital. In this study, artificial neural networks (ANNs) trained by different heuristic algorithms, including Gravitational Search Algorithm (GSA) and Cuckoo Optimization Algorithm (COA), are utilized to estimate monthly electricity demands. The empirical data used in this study are the historical data affecting electricity demand, including rainy time, temperature, humidity, wind speed, etc. The proposed models are applied to Hanoi, Vietnam. Based on the performance indices calculated, the constructed models show high forecasting performances. The obtained results also compare with those of several well-known methods. Our study indicates that the ANN-COA model outperforms the others and provides more accurate forecasting than traditional methods.
Highlights
Electric energy plays a fundamental role in business operations all over the world
The obtained results indicated that the proposed models demonstrated accurate and robust predictions compared to other forecasting models, e.g., mean absolute percentage error and relative root mean square error are reduced by 17% and 22% compared to the shallow neural network model and 9% and 29% compared to the double seasonal Holt–Winters model
The best performing artificial neural networks (ANNs) architecture for the dataset used was identified, which provided the results with the smallest error values during the training
Summary
Electric energy plays a fundamental role in business operations all over the world. Our world runs because electricity makes industries, homes, and services work. Demand forecasting is difficult to implement, the relevance to forecast the electricity demand has been a much-discussed issue in recent years This has led to the development of various new tools and methods for forecasting. The merits of the GSA and COA algorithms and the success of ANNs in electricity demand forecasting have encouraged us to use these heuristic algorithms for training ANNs. In this study, several models for electricity demand forecasting have been developed and tested to provide monthly predictions. Several models for electricity demand forecasting have been developed and tested to provide monthly predictions These models utilize ANNs trained by the three mentioned heuristic algorithms.
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