Multi-Step Peak Power Forecasting With Constrained Conditional Transformer for a Large-Scale Manufacturing Plant

Abstract

Despite of its importance and potential, the research on the peak power forecasting has received little attention. The decrease of the peak power not only reduces operational expense, but also avoids outages especially during the peak demand season. Thus, peak power forecasting, which is the key enabler for such advantages, can bring significant gains especially to a large-scale, energy-intensive manufacturing plant. This paper proposes a high-precision multi-step forecasting method to predict both the the peak power series and time of day the peak occurs. The proposed approach first predicts the peak power for a certain timespan (e.g., a day) by solving a regression problem with multiple features, including daily workload and weather forecast. Then, it generates hourly peak power series for the same timespan to increase the prediction accuracy and to identify the peak hour. In contrast to the daily workload plans, hourly plans rarely exist in practice and thus, hourly peak power forecasting is an auto-regression problem which becomes challenging as prediction timespan increases. In this work, a Constrained and Conditional Transformer (C2Transformer) is proposed for accurate multi-step peak power forecasting. The proposed model takes in the past hourly peak power series of length k along with a single peak predicted over a timespan of length n. Conditioning on the predicted long-term peak, the model generates n hourly power peaks. Also, the proposed C2Transformer has an additional constraint which is minimizing the difference between the predicted peak among n hours and the maximum value among the generated hourly peaks. Through extensive evaluations on a real data set from multiple sources, the proposed C2Transformer has shown superior performance to the widely used deep learning models.

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“…In particular, recurrent neural network (RNN) [ 11 ] and their variants such as long short-term memory (LSTM) [ 12 ] and gated recurrent unit (GRU) [ 13 ] have been widely adopted in time series forecasting problems. Additionally, recent studies have shown that the Transformer model which was initially proposed to handle a large language model can forecast time series data with high accuracy [ 14 ].…”