TY - JOUR
T1 - Introducing technical indicators to electricity price forecasting
T2 - a feature engineering study for linear, ensemble, and deep machine learning models
AU - Demir, Sumeyra
AU - Mincev, Krystof
AU - Kok, J.K. (Koen)
AU - Paterakis, N. G.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Day-ahead electricity market (DAM) volatility and price forecast errors have grown in recent years. Changing market conditions, epitomised by increasing renewable energy production and rising intraday market trading, have spurred this growth. If forecast accuracies of DAM prices are to improve, new features capable of capturing the effects of technical or fundamental price drivers must be identified. In this paper, we focus on identifying/engineering technical features capable of capturing the behavioural biases of DAM traders. Technical indicators (TIs), such as Bollinger Bands, Momentum indicators, or exponential moving averages, are widely used across financial markets to identify behavioural biases. To date, TIs have never been applied to the forecasting of DAM prices. We demonstrate how the simple inclusion of TI features in DAM forecasting can significantly boost the regression accuracies of machine learning models; reducing the root mean squared errors of linear, ensemble, and deep model forecasts by up to 4.50%, 5.42%, and 4.09%, respectively. Moreover, tailored TIs are identified for each of these models, highlighting the added explanatory power offered by technical features.
AB - Day-ahead electricity market (DAM) volatility and price forecast errors have grown in recent years. Changing market conditions, epitomised by increasing renewable energy production and rising intraday market trading, have spurred this growth. If forecast accuracies of DAM prices are to improve, new features capable of capturing the effects of technical or fundamental price drivers must be identified. In this paper, we focus on identifying/engineering technical features capable of capturing the behavioural biases of DAM traders. Technical indicators (TIs), such as Bollinger Bands, Momentum indicators, or exponential moving averages, are widely used across financial markets to identify behavioural biases. To date, TIs have never been applied to the forecasting of DAM prices. We demonstrate how the simple inclusion of TI features in DAM forecasting can significantly boost the regression accuracies of machine learning models; reducing the root mean squared errors of linear, ensemble, and deep model forecasts by up to 4.50%, 5.42%, and 4.09%, respectively. Moreover, tailored TIs are identified for each of these models, highlighting the added explanatory power offered by technical features.
KW - artificial neural networks
KW - day-ahead market
KW - electricity price forecasting
KW - feature engineering
KW - regression models
KW - technical indicators
KW - Technical indicators
KW - Regression models
KW - Day-ahead market
KW - Artificial neural networks
KW - Electricity price forecasting
KW - Feature engineering
UR - http://www.scopus.com/inward/record.url?scp=85078920085&partnerID=8YFLogxK
U2 - 10.3390/app10010255
DO - 10.3390/app10010255
M3 - Article
SN - 2076-3417
VL - 10
JO - Applied Sciences
JF - Applied Sciences
IS - 1
M1 - 255
ER -