Abstract
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.
Highlights
Day-ahead electricity market (DAM) prices have historically been driven by fundamental drivers or fundamentals reflecting an intrinsic demand and supply of electricity
This paper evaluated the explanatory power of Technical indicators (TIs) by examining whether the inclusion of TI
We have demonstrated that TIs can capture the residual impacts from traders’ behavioural biases; resulting in statistically significant reductions in forecast errors with machine learning (ML) models
Summary
Day-ahead electricity market (DAM) prices have historically been driven by fundamental drivers or fundamentals reflecting an intrinsic demand and supply of electricity. Fundamentals are associated with the intrinsic value of a good, commodity, or security. In a world of perfectly rational investors, only fundamentals are considered to drive prices as investors always optimally maximise their utility. Regulatory changes, such as [1], have swept through electricity markets provoking disruptive propagotary shocks. These shocks, which culminated from a growing need to generate cleaner and safer energy [2,3], have indirectly boosted the impacts of technical price drivers or technicals on the DAM and moved prices further from their intrinsic values.
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