دانلود مقاله پیش بینی بار برق ماهانه شهری
چکیده
مقدمه
مطالب مرتبط
تجزیه و تحلیل ویژگی های کوپلینگ بار بر اساس نظریه کوپولا
پیش بینی بار برق ماهانه شهری مدل مبتنی بر SSA-LSVM
تحلیل موردی
نتیجه گیری
منابع
Abstract
Introduction
Related works
Analysis of load coupling characteristics based on Copula theory
Urban monthly power load forecasting model based on SSA-LSSVM
Case analysis
Conclusion
References
چکیده
پیش‌بینی دقیق بار قدرت، پایه و اساس مهم برنامه‌ریزی سیستم قدرت شهری است. با توجه به اینکه تقاضای بار برق شهری ارتباط تنگاتنگی با توسعه اقتصادی و شرایط آب و هوایی دارد و با بکارگیری و ترویج انواع تجهیزات کوپلینگ انرژی و ترویج سیاست جایگزینی انرژی الکتریکی، رابطه بین بار گاز و تقاضای بار توان رو به افزایش است. با این حال، بیشتر روش‌های پیش‌بینی بار موجود، تنها یک عامل اقتصادی یا یک عامل هواشناسی را در نظر می‌گیرند و در ترکیب جفت و مکمل بین بارها ناکام هستند. بر این اساس، این مقاله ابتدا ویژگی های جفت بین بار قدرت و بار اقتصادی، هواشناسی و گاز شهری را با تئوری کوپولا تحلیل می کند. ثانیاً، پارامترهای کلیدی ماشین بردار پشتیبان حداقل مربعات (LSSVM) با استفاده از الگوریتم ازدحام salp (SSA) حل می‌شوند و مدل پیش‌بینی بار قدرت ماهانه شهری بر اساس SSA-LSSVM ایجاد می‌شود. در نهایت، بر اساس داده‌های اقتصاد کلان، داده‌های مشاهدات هواشناسی و بار الکتریکی ماهانه یک شهر در شمال چین، مدل پیش‌بینی تأیید می‌شود. از طریق مقایسه با سناریوهای مختلف پیش‌بینی، تأیید می‌شود که مدل ساخته شده می‌تواند به طور موثر دقت پیش‌بینی را بهبود بخشد و اثرات کاربردی خوبی دارد.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
Accurate power load forecasting is the key foundation and important premise of urban power system planning. Considering that urban power load demand is closely related to economic development and meteorological conditions, and with the application and promotion of various energy coupling equipment and the promotion of electric energy substitution policy, the relationship between gas load and power load demand is increasing. However, most of the existing load forecasting methods only consider a single economic factor or a single meteorological factor, and fail to combine the coupling and complementarity between loads. Based on this, this paper first analyzes the coupling characteristics between power load and urban economic, meteorological, and gas load with Copula theory; Secondly, the key parameters of the least squares support vector machine (LSSVM) are solved by using the salp swarm algorithm (SSA), and the urban monthly power load forecasting model based on SSA-LSSVM is established; Finally, based on the macroeconomic data, meteorological observation data and monthly electrical load of a city in North China, the prediction model is verified. Through comparison with different prediction scenarios, it is confirmed that the built model can effectively improve the prediction accuracy and has good application effects.
Introduction
The construction of a new power system with new energy as the main body, serving carbon peaks and carbon neutral goals means that wind power and photovoltaic power generation will gradually become the main body of the power system in the future. Fundamental changes will take place in the energy and power system, which will not only promote the clean power supply, intelligent power grid and electrification of users, but also change the operation characteristics of power grid [1]. At the same time, reasonable and accurate load forecasting plays an important role in power system production arrangement, economic dispatch, power generation planning, safe operation, and system security evaluation [2]. The monthly power load forecast data is an important input data for formulating the planning and dispatching scheme of the urban power system within one year, which can directly affect the development direction and optimization direction of the future power system and even the city. For the short term, monthly power load forecast is conducive to the safe and stable operation of the power system and effectively reduces operation and maintenance costs. For the medium and long term, it provides a reliable basis for power companies to formulate monthly and annual power generation plans, and has important guiding significance for economic dispatch control of power systems.
Conclusion
This paper mainly establishes the urban monthly power load collaborative forecasting model based on Copula theory and SSA-LSSVM. Based on Copula theory, the model focuses on the interaction between economic development, meteorological conditions, natural gas demand and power load, which makes up for the deficiency of the existing load forecasting models in the interaction of internal and external factors. Secondly, SSA algorithm is used to optimize the key parameters of LSSVM to further improve the accuracy of load forecasting. The prediction accuracy of the prediction model is verified by an example. Take the research results as a new service means to provide more professional and quantitative service products for the power industry, provide scientific basis for power grid operation and dispatching, and enhance the vitality of professional meteorological services.