【摘要】：負荷預(yù)測工作是電力企業(yè)調(diào)度、用電、計劃、規(guī)劃等部門的重要工作內(nèi)容之一,電力負荷預(yù)測水平的高低也是衡量現(xiàn)代電力發(fā)展程度的重要標志。提高電力系統(tǒng)負荷預(yù)測水平,有利于計劃用電管理,節(jié)約一次能源和降低發(fā)電成本,提高電力系統(tǒng)的經(jīng)濟效益和社會效益。論文基于連云港市的實際負荷情況,首先對其做了比較詳細而有條理的分析說明,對影響負荷預(yù)測的諸多因素,例如歷史負荷數(shù)據(jù)、溫度高低、天氣狀況等——考慮進負荷預(yù)測的模型中。為了提高預(yù)測準確率,對負荷數(shù)據(jù)和其他樣本做了大量的預(yù)處理,以便于數(shù)據(jù)平滑而易于被模型所辨識。隨后介紹了誤差反向傳播算法即BP算法的結(jié)構(gòu)和原理,將BP算法用于負荷預(yù)測,簡單高效可行,但由于該算法收斂的時間較長、且容易陷入局部極小點,故提出了用粒子群算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的新型優(yōu)化算法,該算法能夠有針對性地優(yōu)化網(wǎng)絡(luò)結(jié)構(gòu)中的權(quán)值和閾值,在不斷迭代的情況,使得預(yù)測誤差向減小的方向訓(xùn)練,從而使預(yù)測結(jié)果準確率有了較大程度的提高,滿足了負荷預(yù)測的基本要求。
[Abstract]:Load forecasting is one of the most important tasks in the power enterprise such as dispatching, power consumption, planning, planning and so on. The level of power load forecasting is also an important symbol to measure the development of modern electric power. Improving the load forecasting level of power system is beneficial to the management of planned power consumption, saving primary energy and reducing the cost of power generation, and improving the economic and social benefits of the power system. Based on the actual load situation of Lianyungang City, the paper first makes a detailed and orderly analysis of the load forecasting factors, such as historical load data, temperature, Weather conditions, etc.-in models that take into account load forecasting. In order to improve the prediction accuracy, a lot of preprocessing is done to the load data and other samples to make the data smooth and easy to be identified by the model. Then it introduces the structure and principle of error back-propagation algorithm, that is, BP algorithm. The BP algorithm is simple, efficient and feasible for load forecasting. However, because of its long convergence time and easy to fall into local minimum point, Therefore, a new optimization algorithm based on particle swarm optimization (PSO) algorithm for BP neural network is proposed. The algorithm can optimize the weights and thresholds in the network structure, and train the prediction error in the direction of decreasing the prediction error in the case of continuous iteration. Therefore, the accuracy of forecasting results has been improved to a large extent and the basic requirements of load forecasting have been met.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM715

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