Abstract
The planning of integrated energy system is a very complex multi-objective, multi-constraint, nonlinear, random uncertain hybrid combination optimization problem, its planning and design process should consider not only the system capacity, energy exchange, energy storage, energy and other links between the interdependence, but also the interaction and mixing of cold, hot, electricity and other multi-energy flow, which is essentially a non-deterministic polynomial problem. Based on load prediction technology, combined with scene generation, multi-interconnected energy system modeling and other technologies, around the integrated energy system planning and design, consider the comprehensive evaluation of the whole life cycle, an optimal configuration of the integrated energy system is formed.
Highlights
With the increasingly serious problem of environmental pollution, in order to deal with the energy and environmental crisis brought about by the development and utilization of fossil energy, the energy transformation characterized by electricity-centric and large-scale development and utilization of new energy is booming all over the world
With the development of artificial intelligence in recent years, it has been applied in various fields
Literature [4] improves the prediction accuracy by applying fuzzy theory to neural networks; Literature [5] reduces the complexity of the original prediction model by combining empirical mode decomposition with deep learning theory, and speeds up The speed of algorithm load forecasting; Literature [6] applies the wheel mixing theory to load forecasting, and uses the ergodicity and uncertainty of chaos to construct the forecasting model, which better reflects the load changes of users; Literature [7] taking the factors that affect load changes into the load forecasting is more targeted; Literature [8] introduces machine learning into the field of load forecasting, which brings new ideas to load forecasting
Summary
With the increasingly serious problem of environmental pollution, in order to deal with the energy and environmental crisis brought about by the development and utilization of fossil energy, the energy transformation characterized by electricity-centric and large-scale development and utilization of new energy is booming all over the world. Literature [4] improves the prediction accuracy by applying fuzzy theory to neural networks; Literature [5] reduces the complexity of the original prediction model by combining empirical mode decomposition with deep learning theory, and speeds up The speed of algorithm load forecasting; Literature [6] applies the wheel mixing theory to load forecasting, and uses the ergodicity and uncertainty of chaos to construct the forecasting model, which better reflects the load changes of users; Literature [7] taking the factors that affect load changes into the load forecasting is more targeted; Literature [8] introduces machine learning into the field of load forecasting, which brings new ideas to load forecasting. Artificial intelligence technology, represented by machine learning and deep learning, has been widely used in the field of load prediction. At the same time, combining machine learning with optimization theory to achieve self-optimization of machine learning parameters through optimization is an important research direction to improve the accuracy of predictions
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