CNN-LSTM とアンサンブル学習を用いた電力スポット市場価格の予測及び予測信頼区間推定に関する研究
Research on Day-Ahead Electricity Price Forecasting Using CNN-LSTM andEnsemble Learning with Insights on Prediction Confidence Interval Estimation
王子 洋 *・ 前ZiyangWang

匡 鴻 ・ 松 橋
Masahiro MaeRyuji

隆 治 *Matsuhashi

Abstract
Electricity price forecasting (EPF) is critical in energy markets, particularly with the rise of renewable energy sources (RES)in Japan, which can cause day-ahead spot prices to drop to nearly zero JPY/kWh, impacting retailer profitability. This study
demonstrates that the Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model outperforms the LSTM
model in both prediction accuracy and computational efficiency in Japan's electricity spot market. A novel ensemble learning
strategy enhances both the robustness and accuracy of the EPF model is proposed. Novel multimodal explanatory variables,
including electricity spot price, system price, actual power generation, actual solar power generation, meteorological forecasts
and calendar forecasts, alongside the rolling features of spot prices, are utilized and verified. Furthermore, a "policy-versuspolicy" approach addresses the zero-inflated regression issue of the zero price prediction is proposed. Our model, with acomprehensive feature integration, achieves an RMSE of 5.66 JPY/kWh and an R2 of 0.729 during the test range from2022.01.01 to 2022.12.31. The paper also implies a novel method for estimating confidence intervals using ensemble learning.
Keywords : electricity price forecasting (EPF), renewable energy sources (RES), CNN-LSTM, ensemble learning, confidence
interval

1. Introduction

offer a solid foundation, their linear nature can present limitations.

In recent years, the growing integration of renewable energy

The growing penetration of RES, coupled with demand variability,sources (RES), especially wind and solar, has led to a novel

leads to non-linear patterns and abrupt price fluctuations that

occurrence in the electricity markets of the US and EU: negative

traditional statistical models might not accurately capture.

spot prices [1,2]. In contrast, Japan's experience tells a different

Additionally, as depicted in Fig. 1(b), the significant periods ofstory. The substantial addition of solar and wind power to theJapanese grid has resulted in wholesale spot market prices nearing
zero, recorded lowest at 0.01 JPY/kWh. These occurrences will
be referred to as zero prices throughout this paper. The electricity
spot prices in Kyushu region, Japan, is depicted in Fig. 1(a). Fig.
1(b) offers a zooming-in observation of the intermittent zero
prices. Fig. 1(a) shows that the significant occurrence of zero
prices started around 2020, primarily due to the swift adoption ofRES. In this shifting energy environment, day-ahead electricity
price forecasting (EPF) in spot markets has become criticallyimportant [3]. Various statistical methods, including theFig. 1 Kyushu electricity spot price [JPY/kWh] (a) andAutoregressive Moving Average (ARMA) [4,5] and thezooming-in zero inflated prices (b).

Autoregressive Integrated Moving Average (ARIMA) [6–8], havezero prices in the target variable introduce a zero-inflated

been extensively utilized in EPF research. Although these models

regression challenge in machine learning after data normalization.
＊

東京大学大学院工学系研究科電気系工学専攻

Nonetheless, most machine learning algorithms, such as Random〒113-8656 東京都文京区本郷 7-3-1

Forest (RF), Support Vector Regression (SVR), and neuralE-mail：wangziyang@g.ecc.u-tokyo.ac.jp

networks, usually fail to predict consistent zero values.

21

Conventional approaches to address zero-inflated regression

network multiple times and averaging the resulting predictions

typically require a dual-model setup: one classification model to

can help mitigate individual model errors. This approach leads todetect zero values and a separate regression model for non-zero

improved prediction performance, as different models will notvalues. This approach effectively doubles the training time andproduce identical errors on the test set [9,10]. Based on thisassociated costs.

principle, an ensemble learning approach was adopted. The CNN-To tackle the aforementioned challenges, this paper aims toLSTM and LSTM models were trained multiple times, and theimprove EPF in the Kyushu region of Japan by employing

individual predictions were then combined using an averaging

multimodal data as novel features, utilizing LSTM and CNN-

method to form the final ensemble prediction.

LSTM prediction models. Additionally, a new ensemble learning

2.3 "Policy-versus-policy" zero prices forecasting strategy

approach is applied to enhance the prediction accuracy of LSTM

This study presents an innovative approach to address the zero-and CNN-LSTM models, addressing their inherent uncertainties.

inflated regression issue in EPF within the JEPX spot market. TheThe performance of LSTM and CNN-LSTM in EPF is evaluatedsolution starts with an analysis of broader trends in global

based on prediction accuracy and computation time. To addresselectricity spot markets. According to Seel et al. [1], an

the zero-inflated problem in the Japan Electric Power eXchange

overabundance of RES can result in negative electricity spot

(JEPX) spot market, a novel "policy-versus-policy" strategy isprices in the US and EU. From this, we can deduce that negative

introduced, which forecasts zero prices and halves thepricing is a natural outcome of high RES penetration. In Japan,
however, regulations stipulate that electricity spot prices cannot

FC Layer(M6 ⇥ 1)

fall below 0.01 JPY/kWh, preventing negative prices. AssumingLSLSTM

LSTM Layer(M5 ⇥ 1)

TMLSTM Layer(M5 ⇥ 1)
Input Layer(N ⇥ F )Output Layer(48 ⇥ 1)
M6

similar factors that lead to negative prices in the US and EU are

48eic

LSedLSTM

M5TM

M5

prPast N -step data

NFor

TM

ec

ast

present in Japan, it is logical to assume that the explanatory

LSTM
LSF features

variables in these contexts would show similar patterns. When(a)Max Pooling Layer(M1 /2 ⇥ M2 /2)

FC Layer(M6 ⇥ 1)

M3

M6

regression model, the model would predict negative prices since48LSF features

ecFor

LS

TMastedM4

it is not limited by Japan's minimum pricing regulation. Therefore,eTM

M5M2 /2ic

Past N -step dataNM2

these Japanese variables are input into a machine learning

Output Layer(48 ⇥ 1)

M1 /2

prM1

LSTM Layer(M5 ⇥ 1)

Convolution Layer(M3 ⇥ M4 )TMInput Layer(N ⇥ F )LS

Convolution Layer(M1 ⇥ M2 )by using this logic, zero prices can be forecasted by converting(b)

Fig. 2 Schematic of the architectures of the (a) LSTM andany model negative outputs to zeros. This method operates as a(b) CNN-LSTM models.

"policy-versus-policy" forecasting strategy, mirroring actual
market conditions.

computation time compared to the traditional dual-model method.

3. Data Preparation

Furthermore, a natural logarithm transformation is used toThis study leverages multimodal data to improve EPF,

improve the Skewness and Kurtosis of the spot prices, thereby

incorporating actual total power generation, actual solar power

enhancing prediction accuracy. This paper also introduces a newgeneration, rolling features of spot prices (minimum, maximum,method for extracting meteorological forecast data using Google

mean, and standard deviation), system spot price, meteorological

Maps. Finally, a novel method for estimating the confidence

forecast data, and calendar forecast data. The comprehensive datainterval of EPF is demonstrated.

architecture and associated time frame are depicted in Fig. 3. The

2. Methodology

Kyushu area meteorological data were downloaded from the

2.1 LSTM and CNN-LSTM forecasting models

Japan Meteorological Business Support Center (JMBSC) andAn LSTM model and a CNN-LSTM model were developed

were obtained by using OpenCV to identify the land area on theand utilized for EPF, with a comparative analysis conducted usingmap, as is shown in Fig. 4. The input data are divided into three

the Python TensorFlow Keras library. The architectures of thesegreen blocks based on their temporal delay. To ensure consistencymodels are illustrated in Fig. 2, and the hyperparameters werewith the JEPX spot price data, all inputs were linearly interpolatedchosen empirically to achieve optimal performance.

to a 30-min time resolution. A 7-day-long moving window was

2.2 Ensemble learning strategy

applied to the input data before being input into the CNN-LSTM

Due to the inherent variability of neural network models, whichand LSTM models. In the JEPX spot market, all transactions mustarises from their sensitivity to initial conditions and the stochastic

be finalized by the 10:00 JST bidding deadline. To ensure anature of their training processes, training the same neuralthorough and precise prediction process, sufficient time is2

JEPX day-aheadmarket biddingdeadline

Forecasting point(5 h ahead)

7-day-long historical data (05:00 - 05:00]
Actual power generation (Kyushu), actual solar power generation(Kyushu)

Interval(19 h)

4. Results and Discussion

Prediction period[00:00 - 00:00)

4.1 Ensemble learning results
7-day-long forecast data ① [00:00 - 00:00)

An ensemble learning technique was applied to each feature set

Day-ahead areal price (Kyushu), day-ahead areal price rolling features(Kyushu), day-ahead system priceto generate ensemble predictions. Table 1 presents the prediction
7-day-long forecast data ② [00:00 - 00:00)

accuracy and the computation time of the proposed LSTM andMeteorological forecast data (Kyushu), calendar forecast data

05:00

00:00

00:00

・・・・・・

05:00 10:00

00:00

CNN-LSTM models. According to Table 1, the CNN-LSTM only

00:00

Fig. 3 Illustration of the data architecture with a 30-min

has half the computation time of the LSTM for same training

time interval, highlighting the time delays among different

times of ensemble learning, while still boasts higher predictiondata.

accuracy over the LSTM using less computation time.
Table 1 Prediction accuracy and computation time comparison.

allocated for the execution and refinement of neural network
calculations. Considering the computational demands and theModel

Computation

Ensembletimetimes

59 min15

complexities involved, a 5-hour buffer before the deadline isestablished. The forecasting time is set to 05:00 JST, covering theLSTM

R2

RMSE

0.5782

7.062

entire following day from 00:00 JST to 23:30 JST, encompassing

JPY/kWha total of 48 time frames.

LSTM

120 min300.5821

7.028
JPY/kWh

CNN-60 min

300.5825

LSTM
CNNLSTM

7.027
JPY/kWh40 min

200.5824

7.031
JPY/kWhThe results of the predictions using all features in the day-byFig. 4 Kyushu area map (a) and the identified land areas

day prediction method are illustrated in Fig. 5. It is evident from(b). Map data: ©2024 Google, TMap Mobility.

Fig. 5 that each training iteration generates a distinct prediction

In this study, photovoltaic (PV) power is the primary RES inon the test set, highlighting the inherent uncertainty in the neural

the Kyushu region due to its significantly higher installed

network training process. The ensemble learning method wecapacity compared to wind power. Consequently, features related

proposed effectively minimizes the variability present in

to wind power are not included in this investigation.

individual model predictions. While some predictions mayFig. 5 Individual and ensemble predictions for the test range of May and September in 2022, using all the proposed features andday-by-day prediction approach.

3

significantly deviate from the actual electricity spot prices, theprice data has been shown crucial for EPF in the Japan electricity

ensemble approach, averages these forecasts to yield a more

spot market. A "policy-versus-policy" strategy has been proposed

accurate final prediction. The model's capability to predict zero

to solve the zero-inflated regression problem, which halfs theprices validates the "policy-versus-policy" zero prices forecasting

computation time compared with traditional two-stage method.

strategy. The performance metrics of the ensemble learning for

By using the day-by-day prediction approach, the ensemble

the day-by-day prediction method using all features are presented

learning method achieved a RMSE and R2 of 5.66 JPY/kWh andin Table 2. This day-by-day prediction approach significantly

0.728 over the test range of the full year 2022. An implication forenhances prediction accuracy compared to the one-time

a novel estimation method of the EPF's confidence interval using

prediction method.

the ensemble learning approach has been proposed.

Table 2 Ensemble learning metrics for the day-by-day prediction
approach using all proposed features.

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Metrics
RMSE

R2

5.66 JPY/kWh0.729

4.2 Implications for EPF's confidence interval
In Fig. 5, it is noteworthy that when the ensemble learning
prediction accuracy is high, the 30 individual predictions tend tohighly overlap each other and also demonstrate high prediction
accuracy, as shown for the period 2022.05.09-2022.05.14 in Fig.
5(b). Conversely, when the ensemble learning fails to predict theactual value accurately, the 30 individual predictions tend todiverge from each other without overlapping. The standard
deviation of the 30 individual predictions at the same time pointis calculated and used to compute the Person correlation efficientwith the ensemble learning prediction error at the same time point.
The correlation efficient value is 0.462, indicating a moderatecorrelation. By utilizing the above-mentioned phenomenon, anovel confidence interval estimation method for EPF can bedeveloped in our future work.
5. Conclusions and Future Work
This study has proposed an innovative EPF framework thatutilizes multimodal data augmented by an ensemble learningtechnique. The CNN-LSTM model is superior over the LSTM
model in both prediction accuracy and computation time. The
logarithm transform of the pre-processing for the electricity spot4