題目：Short-term load forecasting based on multivariate adaptive step FOA optimized GRNN

時(shí)間：5月5號(hào)（星期六）下午 3：00-4：00

地點(diǎn)：X2511

摘要：Short-term load forecasting plays a significant role in power system. In this paper, we propose multivariate adaptive step fruit fly optimization algorithm (MAFOA) to optimize the smoothing parameter of generalized regression neural network (GRNN) in short-term power load forecasting. In addition, due to the great impact of some external factors including temperature, weather types and date types on short-term power load, we take these factors into account. Moreover, we propose an efficient interval partition technique to handle with the structured and unstructured data. The empirical results demonstrate that convergence speed and forecasting accuracy of the proposed model are superior to BP neural network, GRNN and fruit fly algorithm optimized GRNN.

作者簡(jiǎn)介： 蔣鋒，博士，教授，文瀾學(xué)者。中南財(cái)經(jīng)政法大學(xué)統(tǒng)計(jì)與kaiyun開云官方網(wǎng)站數(shù)理與金融統(tǒng)計(jì)系主任，應(yīng)用統(tǒng)計(jì)專業(yè)碩士大數(shù)據(jù)導(dǎo)師組組長(zhǎng)。澳大利亞Monash University訪問學(xué)者。

主持國(guó)家自然科學(xué)基金面上項(xiàng)目一項(xiàng)；主持湖北省科研項(xiàng)目2項(xiàng)；主持完成國(guó)家自然科學(xué)基金青年項(xiàng)目一項(xiàng)；主持完成湖北省自然科學(xué)基金一項(xiàng)，主持完成中央高?？蒲谢痦?xiàng)目?jī)身?xiàng)，主持完成中國(guó)博士后基金項(xiàng)目一項(xiàng)；曾獲湖北省優(yōu)秀博士學(xué)位論文獎(jiǎng)；曾參與國(guó)家杰出青年基金項(xiàng)目、省杰出青年科學(xué)基金項(xiàng)目和多項(xiàng)國(guó)家自然科學(xué)基金面上項(xiàng)目。擔(dān)任國(guó)際期刊“Neural Comput Appl”、“IEEE Trans Neural Netw Learn Syst”、“Comput Appl Math.”等和國(guó)際會(huì)議的評(píng)審人，擔(dān)任國(guó)際學(xué)術(shù)會(huì)議“ICACI2018”、“ICICIP2016”等的PC Member。目前出版學(xué)術(shù)專著一部，已經(jīng)發(fā)表SCI或EI論文50余篇，其中30多篇論文被SCI收錄?，F(xiàn)為TCCT隨機(jī)控制分委員會(huì)委員（SCSSC）和美國(guó)數(shù)學(xué)評(píng)論評(píng)論員。