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研究生：	王佳樂 Aprillia, Happy
論文名稱：	確定性和概率性短期負載與太陽光電發電預測方法 Deterministic and Probabilistic Short-Term Forecasting Approaches for Load Demands and PV Generation
指導教授：	楊宏澤 Yang, Hong-Tzer
學位類別：	博士 Doctor
系所名稱：	電機資訊學院 - 電機工程學系 Department of Electrical Engineering
論文出版年：	2020
畢業學年度：	108
語文別：	英文
論文頁數：	104
中文關鍵詞：	負荷預測、優化算法、光伏發電預測、概率負荷預測.
外文關鍵詞：	load forecasting, optimization algorithm, photovoltaic power forecasting, probabilistic load forecasting
相關次數：	點閱：101 下載：21
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本文提出了能量管理系統（EMS）中負荷需求和光伏發電的確定性和概率性預測方法。提出了一種可靠的確定性預測算法，用於儘管負荷需求和PV發電都繼承了非平穩和季節性模式，但仍可針對每種負荷和PV發電獲得準確的預測結果。因此，鯨魚優化算法–離散小波變換–多元線性回歸（WOA-DWT-MLR）被用作預測算法，在系統和最終用戶負載數據集上進行了測試，並通過平均絕對百分比誤差（MAPE）進行了評估。對於光伏發電，使用卷積神經網絡-Salp群算法（CNN-SSA），在台灣南部的200 kW光伏電站中進行測試，並通過平均相對誤差（MRE）和MAPE進行評估。
與其僅通過均方根誤差（MRE）和平均絕對平均百分比（MAPE）來解釋預測準確性，不如對預測結果將要發生的預期風險進行觀察。作為概率方法，構建具有風險評估指數（RAI）的預測區間（PI）來理解負荷需求中的不確定性行為。 PI由分位數回歸隨機森林（QRRF）建模，並輔以概率圖和風險評估指數（RAI）。概率映射用於捕獲每日負荷的每小時分佈，而RSS用於通過具有相應的預測結果來計算明天可能發生的風險。在系統數據集上測試了建議的概率負荷預測。
所提出的預測方法有利於EMS的運行。仿真結果表明，所提出的短期確定性預測方法可以有效地提高預測準確性，而所提出的短期概率負荷預測可以從風險嚴重性評分中間接量化第二天可能發生的不確定性，從而更加清晰解釋預測結果。

As the forecasting algorithm is necessary to provide prosumer’s load profile in active contribution in energy management system, this dissertation proposes the deterministic and probabilistic short-term forecasting approaches for load demand and photovoltaic (PV) power generation. To provide a computational effective and high accuracy forecasting performance, whale optimization algorithm – discrete wavelet transforms – multiple linear regression (WOA-DWT-MLR) are used as short-term load forecasting algorithms, tested on both system- and end-user load data sets. Like load pattern, the PV generation also inherits uncertain behavior and seasonal pattern. Thus, salp swarm algorithm - convolutional neural network (SSA-CNN) is used to extract the optimal features of PV power generation without time-consuming trial and error in CNN hyperparameter tuning.
Instead of interpreting the forecasting accuracy only by mean root error (MRE) and mean absolute average percentage (MAPE), an observation about the intended risk that is going to be happened by the forecasting results are studied. As the probabilistic approach, a prediction interval (PI) with a risk assessment index (RAI) is constructed to comprehend the uncertainty behavior in load demand. The PI is modeled by quantile regression random forest (QRRF), complemented by the probability mapping and risk Assessment Index (RAI). Probability mapping is used to capture the hourly distribution of daily load while RAI is used to calculate the risk that might happen tomorrow by having the corresponding forecasting results.
The proposed methods are tested using data set of Independent System Operator New England (ISO-NE), Shalun office, and a 200-kW PV power plant in south Taiwan to represent system load, end-user load, and PV power generation, respectively. The simulation results indicate that the proposed short-term deterministic forecasting approaches can effectively improve the forecasting accuracy, while the proposed short term probabilistic load forecasting can indirectly quantify the uncertainty that may happen on the next day without extensive computation time.

TABLE OF CONTENTS
摘要	i
ABSTRACT	ii
TABLE OF CONTENTS	iii
LIST OF FIGURES	vi
LIST OF TABLES	ix
LIST OF SYMBOLS	x
CHAPTER 1 INTRODUCTION	1
1	Background and Motivation	1
2	Review of Literatures	3
3	Research Objectives and Methods	8
4	Major Contributions	10
5	Organization of Dissertation	11
CHAPTER 2 SYSTEM DESCRIPTION AND ARCHITECTURE	12
1	System Descriptions and Architecture	12
2 Constructing a Reliable Forecasting Algorithm	14
2.1 	Choice of Suitable Features of Forecasting Algorithm	15
2.1.1 	Correlation Coefficient	18
2.1.2 	Student T-test	18
2.2	Evaluation Metrics	18
3	Summary	19
CHAPTER 3 PROPOSED SHORT-TERM LOAD FORECASTING	20
1	Introduction	20
2	Discrete Wavelet Transform	20
3	Whale Optimization Algorithm	21
4	Optimal Signal Decomposition for Short Term Load Forecasting	23
5	Summary	26
CHAPTER 4 PROPOSED SHORT-TERM PHOTOVOLTAIC POWER FORECASTING	27
1	Introduction	27
2	Convolutional Neural Network	27
3	Salp Swarm Algorithm	29
4	CNN’s Predictors Arrangement	31
5	Training Results of the Proposed SSA-CNN	34
6	Summary	37
CHAPTER 5 PROPOSED PROBABILISTIC LOAD FORECASTING	38
1	Introduction	38
2	Interval Load Forecasting Review	38
3	The Proposed Probabilistic Load Forecasting	41
3.1	Input Variable Arrangement	42
3.2	Probability Map	42
3.3	Quantile Regression Random Forest	45
4	Risk Assessment Index	47
5	Training Stage of the Proposed PLF	49
6	Summary	54
CHAPTER 6 RESULTS AND DISCUSSIONS	55
1	Introduction	55
2	Simulation Settings and Test Systems	55
3	Simulation Results of Deterministic Load and PV Power Forecasting	56
3.1	WOA-DWT-MLR for STLF	57
3.1.1	Forecasting predictors arrangement	57
3.1.2	Benchmark Algorithms	60
3.1.3	Simulation results	61
3.1.3.1	Forecasting performance on system data set	62
3.1.3.2	Forecasting performance on end-user data set	64
3.2	SSA-CNN for STPVPF	71
3.2.1	Benchmark Algorithms	71
3.2.2	Simulation results	71
3.3	Summary	79
4	Simulation Results of Probabilistic Load Forecasting	80
4.1	Forecasting performance on working-day data set	80
4.2	Forecasting performance on special days	85
4.3	Evaluation using time-varying Markov chains	89
4.4	Summary	92
CHAPTER 7 CONCLUSIONS AND FUTURE PROSPECTS	94
1	Conclusions	94
2	Future Prospects	95
REFERENCES	96


                                    

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