設(shè)計(jì)并實(shí)現(xiàn)了神經(jīng)網(wǎng)絡(luò)和模糊邏輯相結(jié)合的綜合預(yù)測(cè)模型進(jìn)行短期電力負(fù)荷預(yù)測(cè)。由
神經(jīng)網(wǎng)絡(luò)和模糊邏輯分別對(duì)基本負(fù)荷和受天氣、節(jié)假日影響的負(fù)荷進(jìn)行預(yù)測(cè)，使其在天氣突變等情況下也能達(dá)到較高的預(yù)測(cè)精度。采用此模型對(duì)石家莊電力系統(tǒng)負(fù)荷進(jìn)行預(yù)測(cè)分析，取得了令人滿意的結(jié)果。
關(guān)鍵詞：神經(jīng)網(wǎng)絡(luò)；模糊邏輯；短期負(fù)荷預(yù)測(cè)
Abstract: A hybrid model for short-term load forecasting that integrates artificial neural networks (ANN) and fuzzy expert systems is presented in this paper. ANN and fuzzy logic are separately used to forecast the normal load and the load influenced by temperature and holidays, to improve the forecast precision in those cases. In the test forecasting of Shijiazhuang power system, the proposed model provided good forecasting accuracy.
Key words: Neural Networks; Fuzzy Logic; Short-term Load Forecasting