【摘要】：太陽能熱水器是我國本土化并具有自主知識(shí)產(chǎn)權(quán)的產(chǎn)業(yè)。隨著經(jīng)濟(jì)的發(fā)展，社會(huì)節(jié)能意識(shí)進(jìn)一步提高，，大力促進(jìn)了我國太陽能熱水器產(chǎn)業(yè)的發(fā)展�！笆逡�(guī)劃”明確將太陽能產(chǎn)業(yè)作為我國戰(zhàn)略新興產(chǎn)業(yè)之一，出臺(tái)大量的扶持政策。這些政策措施大力促進(jìn)了太陽能熱水器產(chǎn)業(yè)的蓬勃發(fā)展，行業(yè)的競爭也越來越激烈。而企業(yè)想在激烈的競爭中取勝，將必須爭取以最合理的成本將產(chǎn)品交付給客戶，這就要求企業(yè)要對(duì)市場的變化和業(yè)務(wù)本身的發(fā)展前景進(jìn)行正確的評(píng)估和預(yù)測，這是現(xiàn)代企業(yè)成功的關(guān)鍵因素。預(yù)測是決策的前提，成功的決策離不開科學(xué)的預(yù)測。預(yù)測可以提高企業(yè)對(duì)不確定事件的反應(yīng)能力，從而減少不利事件帶來的損失，增加利用有利機(jī)會(huì)帶來的收益。 預(yù)測是針對(duì)市場變化規(guī)律可統(tǒng)計(jì)的范疇來進(jìn)行的。傳統(tǒng)市場預(yù)測模型多是利用時(shí)間序列內(nèi)的歷史需求數(shù)據(jù)來預(yù)測未來市場，且預(yù)測因子受個(gè)人經(jīng)驗(yàn)判斷的影響較大，使得預(yù)測技術(shù)的實(shí)際應(yīng)用困難且預(yù)測精度較差。而人工神經(jīng)網(wǎng)絡(luò)因具有很強(qiáng)的自學(xué)習(xí)、自訓(xùn)練和非線性追溯能力，將有助于提高市場預(yù)測的精度和效率。本文通過與傳統(tǒng)市場需求預(yù)測模型的比較，針對(duì)我國太陽能熱水器市場需求的實(shí)際情況，設(shè)計(jì)研究出適用于太陽能熱水器市場需求預(yù)測模型。首先，介紹了市場需求預(yù)測的相關(guān)理論和主要預(yù)測方法；其次，研究太陽能市場需求預(yù)測算法模型，比較Gompertz回歸算法、指數(shù)平滑算法；再次，研究BP神經(jīng)網(wǎng)絡(luò)模型及其算法，應(yīng)用該模型對(duì)太陽能熱水器市場進(jìn)行需求預(yù)測,并于Gompertz模型算法和指數(shù)平滑算法的預(yù)測結(jié)果相比較。
[Abstract]:Solar water heater is a native and independent intellectual property industry in China. With the development of economy, the consciousness of social energy saving has been further improved, which has greatly promoted the development of solar water heater industry in China. The 12th five-year Plan explicitly regards solar energy industry as one of our country's strategic emerging industries, introducing a large number of supporting policies. These policies and measures have greatly promoted the vigorous development of the solar water heater industry, and the competition in the industry is becoming more and more fierce. If the enterprise wants to win in the fierce competition, it must try to deliver the product to the customer at the most reasonable cost, which requires the enterprise to correctly evaluate and forecast the market change and the development prospect of the business itself. This is a key factor in the success of modern enterprises. Prediction is the premise of decision-making, and scientific prediction is indispensable to successful decision-making. Prediction can improve the ability of enterprises to respond to uncertain events, thus reducing the losses brought by adverse events and increasing the benefits of utilizing favorable opportunities. The forecast is made according to the statistical category of the law of market change. The traditional market forecasting models mostly use the historical demand data in time series to predict the future market, and the prediction factors are greatly influenced by personal experience judgment, which makes the practical application of forecasting technology difficult and the prediction accuracy is poor. Because of its strong self-learning, self-training and nonlinear traceability, artificial neural networks will help to improve the accuracy and efficiency of market forecasting. By comparing with the traditional market demand forecasting model, according to the actual situation of the solar water heater market demand in our country, this paper designs and studies the market demand forecasting model suitable for the solar water heater market. Firstly, it introduces the related theories and main forecasting methods of market demand forecasting. Secondly, it studies the algorithm model of solar energy market demand forecasting, compares Gompertz regression algorithm and exponential smoothing algorithm. Thirdly, it studies the BP neural network model and its algorithm. The model is used to predict the demand of solar water heater market, and compared with the prediction results of Gompertz model algorithm and exponential smoothing algorithm.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TP183

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