DEVELOPMENT OF INTELLIGENT LOAD FORECASTING MODELS FOR DEMAND-SIDE MANAGEMENT.
Abstract:
Demand-side management (DSM) plays a crucial role in optimizing energy consumption and improving the efficiency of power systems. One of the key components of DSM is load forecasting, which involves predicting the future electricity demand with high accuracy. Traditional load forecasting methods often rely on statistical techniques and historical data, but they may struggle to capture the dynamic and complex nature of electricity consumption patterns.
This paper presents the development of intelligent load forecasting models for demand-side management, leveraging advanced machine learning and artificial intelligence techniques. The objective is to enhance the accuracy and reliability of load forecasting, enabling more effective DSM strategies and facilitating the integration of renewable energy sources into the power grid.
The proposed models utilize a combination of data-driven approaches, including time series analysis, neural networks, and ensemble learning. The models are trained on historical load data, weather information, and other relevant variables to capture the dependencies and patterns in electricity consumption. Additionally, the models incorporate real-time data streams to adapt to changing conditions and improve short-term load predictions.
To evaluate the performance of the developed models, extensive experiments are conducted using a large-scale dataset from a real-world power system. The results demonstrate significant improvements in load forecasting accuracy compared to traditional methods. The intelligent forecasting models exhibit enhanced capabilities in capturing seasonality, time-of-day variations, and sudden load fluctuations caused by external factors.
Furthermore, the paper discusses the integration of the developed load forecasting models into a comprehensive DSM framework. The models provide accurate load predictions for different time horizons, enabling utilities and grid operators to effectively manage electricity generation, distribution, and demand-response programs. This contributes to load balancing, reducing peak demand, minimizing energy costs, and optimizing the utilization of available resources.
In conclusion, the development of intelligent load forecasting models for demand-side management presents a promising approach to enhance the efficiency and sustainability of power systems. By leveraging advanced machine learning techniques, these models offer accurate load predictions, enabling utilities to make informed decisions and implement effective DSM strategies. The integration of intelligent forecasting models into DSM frameworks facilitates the integration of renewable energy sources and promotes a more resilient and optimized power grid.
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