Wind speed forecasts can boost the quality of wind energy generation by increasing the efficiency and enhancing the economic viability of this variable renewable resource. This work proposes a hybrid model for wind energy capacity for electrical power generation at coastal sites by utilizing wind-related variables characteristics. The datasets of three coastal locations of Kuwait validate the proposed method. The hybrid model is a merger of Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) and predicts one-month-ahead wind speed for wind power density calculation. The neural network starts its performance evaluation with a variable number of hidden-layer neurons to finally identify the optimal ANN topology. Comparisons of statistical indices with both expected and observed test results indicate that the ANN-PSO based hybrid model with the low root-mean-square-error and mean-square-error values outperforms ANN-based trivial models. The prediction model developed in this work is highly accurate with a Mean Absolute Percentage Error (MAPE) of approximately (3-6%) for all the sites.

The total installed capacity of wind energy of the world from 2010 to 2019.

The conventional backpropagation neural network (BPNN) uses the weight update rule of gradient and a decent technique to determine the system's weights under investigation by minimizing the error criterion. However, this technique primarily gets stuck in a local minimum. On the other hand, Particle swarm optimization (PSO) is a robust search and optimization technique. PSO can effectively overcome the problem of local minima of BPNN. In PSO algorithm, each particle searches its space to find the best local fitness, called Pbest. Every particle cannot achieve globally best fitness, called Gbest. Every single particle track and memorize its current best fitness in the swarm. In this proposed hybrid model, the solution vector of PSO consists of weights and biases of ANN model. For best training of ANN, weights and biases are predicted by PSO algorithms.

The total installed added capacity.

Improved Energy Production Planning: Wind speed forecasting is crucial for planning energy production from wind farms. Accurate forecasts allow energy companies to optimize their operations, ensuring that they produce the maximum amount of energy while minimizing costs.

Cost Reduction: By accurately predicting wind speeds one month in advance, energy companies can better plan maintenance schedules, resource allocation, and energy trading strategies. This can lead to cost reductions by minimizing downtime, optimizing resource usage, and capitalizing on favorable market conditions.

Enhanced Grid Integration: Wind energy integration into the power grid can be challenging due to its intermittency. Accurate wind speed forecasts help grid operators anticipate fluctuations in wind power generation, enabling better management of grid stability and reliability.

Risk Mitigation: Long-term wind speed forecasting helps mitigate risks associated with energy production, such as underproduction or overproduction. By having reliable forecasts, energy companies can hedge against financial risks associated with variable wind conditions.

Wind speed Distribution Analysis (Al wafrah).

Growing Renewable Energy Sector: With increasing concerns about climate change and the transition towards cleaner energy sources, the demand for reliable renewable energy, including wind energy, is on the rise. Accurate wind speed forecasting is crucial for maximizing the efficiency and reliability of wind power generation, driving the adoption of advanced forecasting technologies.

Expansion of Wind Energy Infrastructure: Many coastal regions around the world have favorable wind conditions, making them ideal locations for wind farm development. As countries strive to meet renewable energy targets and reduce dependence on fossil fuels, there is a growing need for sophisticated wind speed forecasting tools to support the planning, operation, and maintenance of these wind farms.

Grid Integration Challenges: Integrating large amounts of wind energy into the power grid poses challenges due to the variability and intermittency of wind resources. Reliable one-month-ahead wind speed forecasting can help grid operators anticipate fluctuations in wind power generation and better manage grid stability, making it an essential tool for the successful integration of wind energy into the grid.

Risk Management in Energy Markets: Energy companies and investors involved in wind energy projects face risks associated with variable wind conditions, such as underproduction or overproduction of electricity. Accurate one-month-ahead wind speed forecasting can help mitigate these risks by providing valuable insights into future wind patterns, enabling better decision-making and risk management strategies.

One month-ahead prediction results based on SVM model at Wafra.