article · Computational Intelligence and Neuroscience
Rapid growth in electricity demand alongside the integration of renewable energy introduces grid stability challenges that smart grids aim to resolve. To support this transition, a hybrid forecasting approach combines artificial neural networks, support vector machines, and particle swarm optimisation to predict long-term photovoltaic power generation. The model incorporates fluctuations in power demand together with atmospheric factors to match solar output with consumption patterns. Evaluated using real-world consumption and meteorological data from Douala, Cameroon, the approach achieved strong accuracy, recording a mean absolute percentage error of 3.32 per cent and a regression coefficient of 0.9984. These findings demonstrate better performance than existing alternatives in the published literature, indicating usefulness for managing renewable energy needs. However, unpredictable variations within the underlying datasets can reduce the convergence speed of the algorithm during execution.
Integrating solar power into electrical grids requires reliable forecasts that account for fluctuating weather and consumer demand. Accurate long-term solar generation estimates help smart grids maintain power quality and balance supply with demand. Demonstrating reliable performance on real-world climate data supports the practical planning and stability of power networks adopting higher shares of renewable energy.
The methodology is at an applied research stage, validated on historical climate and consumption data from Douala. It could inform algorithmic tools used by smart grid operators, energy utilities, and solar farm planners to forecast long-term generation against demand. Real-world deployment would require addressing the identified convergence speed limitations caused by data variability before integration into commercial energy management software.
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The exponential growth of electrical demand and the integration of renewable energy sources (RES) brought new challenges in the traditional grid about energy quality. The transition from traditional grid to smart grid is the best solution which provides necessary tools and information and communication technologies (ICT) for service enhancement. In this study, variation of energy demand and some factors of atmospheric change are considered to forecast production of photovoltaic energy that can be adapted for evolution of consumption in smart grid. The contribution of this study concerns a novel optimized hybrid intelligent model made of the artificial neural network (ANN), support vector machine (SVM), and particle swarm optimization (PSO) implemented for long term photovoltaic (PV) power generation forecasting based on real data of consumption and climate factors of the city of Douala in Cameroon. The accuracy of this model is evaluated using the coefficients such as the mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and regression coefficient (R). Using this novel hybrid technique, the MSE, RMSE, MAPE, MAE, and R are 14.9721, 3.8693, 3.32%, 0.867, and 0.9984, respectively. These obtained results show that the novel hybrid model outperforms other models in the literature and can be helpful for future renewable energy requirements. However, the convergence speed of the proposed approach can be affected due to the random variability of available data.
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DOI: 10.1155/2022/7495548
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