Prediction of Spot Prices in Nord Pool’s Day-Ahead Market Using Machine Learning and Deep Learning

Rantonen, Mika; Korpihalkola, Joni

doi:10.1007/978-3-030-64583-0_59

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…use a categorical boosting feature selection and a bidirectional LSTM network for point forecasts. Rantonen & Korpihalkola (2020) propose a weighted averaging scheme of statistical and ML forecasts with an additional meta learner neural network governing the weights. Among recent extensions to well-established methods, He et al (2020) combined a CNN with a label-distribution-learning-forest decoder to produce probabilistic predictions.…”

Section: Electricity Price Forecastingmentioning

confidence: 99%

Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx

Olivares,

Challu,

Marcjasz

et al. 2021

Preprint

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We extend the neural basis expansion analysis (NBEATS) to incorporate exogenous factors. The resulting method, called NBEATSx, improves on a well performing deep learning model, extending its capabilities by including exogenous variables and allowing it to integrate multiple sources of useful information. To showcase the utility of the NBEATSx model, we conduct a comprehensive study of its application to electricity price forecasting (EPF) tasks across a broad range of years and markets. We observe state-of-the-art performance, significantly improving the forecast accuracy by nearly 20% over the original NBEATS model, and by up to 5% over other well established statistical and machine learning methods specialized for these tasks. Additionally, the proposed neural network has an interpretable configuration that can structurally decompose time series, visualizing the relative impact of trend and seasonal components and revealing the modeled processes' interactions with exogenous factors. To assist related work we made the code available in this repository.

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Section: Electricity Price Forecastingmentioning

confidence: 99%

Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx

Olivares,

Challu,

Marcjasz

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Apart from power forecasting, various statistical methods also have been applied to electricity price forecasting with varying degrees of success [4][5][6][7][8]. Then, an Artificial Neural Network (ANN)-based application is proposed to forecast a day-ahead electricity price [9][10][11][12][13]. [14] forecast load with Multi-Layer Perceptron (MLP) compared with a linear model.…”

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning for electricity energy trading

Sanayha

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The deregulation and liberalization of the energy market in the 1990s prompted short-term electricity trading, allowing energy markets to produce net output over a range of time periods as a result of this decentralized system, most commonly minutes to days ahead of time. The energy industry urgently requires a system that has undergone substantial modernization in place to handle a variety of issues, including the current climate, renewable resources, and the energy framework. In this dissertation, we investigate a deep reinforcement learning framework for both wholesale and local energy trading, which probes the challenge of RL to optimize the real-world problem in the energy exchange. First, we introduce the MB-A3C algorithm for day-ahead energy bidding to reduce WPP's costs. Also, we have illustrated that our model can generate a strategy that obtains a more than 15% reduction in average cost per day in Denmark and Sweden (Nord Pool dataset). Second, the MB-A3C3 approach is carried out and conducted on a large-scale, real-world, hourly 20122013 dataset of 300 households in Sydney, Australia. When internal trade (trading among houses) increased and external trade (trading to the grid) decreased, our multiple agent RL (MB-A3C3) significantly lowered energy bills by 17%. In closing the gap between real-world and theoretical problems, the algorithms herein aid in reducing wind power production costs and customers' electricity bills.

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Prediction of Spot Prices in Nord Pool’s Day-Ahead Market Using Machine Learning and Deep Learning

Cited by 2 publications

References 12 publications

Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx

Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx

Deep reinforcement learning for electricity energy trading

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