Analysis for Non-Residential Short-Term Load Forecasting Using Machine Learning and Statistical Methods with Financial Impact on the Power Market

Short-term load forecasting predetermines how power systems operate because electricity production needs to sustain demand at all times and costs. Most load forecasts for the non-residential consumers are empirically done either by a customer’s employee or supplier personnel based on experience and historical data, which is frequently not consistent. Our objective is to develop viable and market-oriented machine learning models for short-term forecasting for non-residential consumers. Multiple algorithms were implemented and compared to identify the best model for a cluster of industrial and commercial consumers. The article concludes that the sliding window approach for supervised learning with recurrent neural networks can learn short and long-term dependencies in time series. The best method implemented for the 24 h forecast is a Gated Recurrent Unit (GRU) applied for aggregated loads over three months of testing data resulted in 5.28% MAPE and minimized the cost with 5326.17 € compared with the second-best method LSTM. We propose a new model to evaluate the gap between evaluation metrics and the financial impact of forecast errors in the power market environment. The model simulates bidding on the power market based on the 24 h forecast and using the Romanian day-ahead market and balancing prices through the testing dataset.

Download Full-text

Short-Term Electricity Load Forecasting with Machine Learning

Information ◽

10.3390/info12020050 ◽

2021 ◽

Vol 12 (2) ◽

pp. 50

Author(s):

Ernesto Aguilar Madrid ◽

Nuno Antonio

Keyword(s):

Machine Learning ◽

Load Forecasting ◽

Production Costs ◽

Electricity Production ◽

Gradient Boosting ◽

Multiple Sources ◽

Short Term ◽

Extreme Gradient Boosting ◽

Short Term Load Forecasting ◽

Electricity Load

An accurate short-term load forecasting (STLF) is one of the most critical inputs for power plant units’ planning commitment. STLF reduces the overall planning uncertainty added by the intermittent production of renewable sources; thus, it helps to minimize the hydrothermal electricity production costs in a power grid. Although there is some research in the field and even several research applications, there is a continual need to improve forecasts. This research proposes a set of machine learning (ML) models to improve the accuracy of 168 h forecasts. The developed models employ features from multiple sources, such as historical load, weather, and holidays. Of the five ML models developed and tested in various load profile contexts, the Extreme Gradient Boosting Regressor (XGBoost) algorithm showed the best results, surpassing previous historical weekly predictions based on neural networks. Additionally, because XGBoost models are based on an ensemble of decision trees, it facilitated the model’s interpretation, which provided a relevant additional result, the features’ importance in the forecasting.

Download Full-text

Intelligent Machine Learning with Evolutionary Algorithm based Short Term Load Forecasting in Power Systems

IEEE Access ◽

10.1109/access.2021.3096918 ◽

2021 ◽

pp. 1-1

Author(s):

Ibrahim M. Mehedi ◽

Hussain Bassi ◽

Muhyaddin J. Rawa ◽

Mohammed Ajour ◽

Abdullah Abusorrah ◽

...

Keyword(s):

Machine Learning ◽

Power Systems ◽

Evolutionary Algorithm ◽

Load Forecasting ◽

Short Term ◽

Intelligent Machine ◽

Short Term Load Forecasting

Download Full-text

Aggregated short-term load forecasting for heterogeneous buildings using machine learning with peak estimation

Energy and Buildings ◽

10.1016/j.enbuild.2021.110742 ◽

2021 ◽

pp. 110742

Author(s):

Amine Bellahsen ◽

Hanane Dagdougui

Keyword(s):

Machine Learning ◽

Load Forecasting ◽

Short Term ◽

Short Term Load Forecasting

Download Full-text

Short Term Load Forecasting of Industrial Electricity Using Machine Learning

Smart Cities - Communications in Computer and Information Science ◽

10.1007/978-3-030-38889-8_12 ◽

2020 ◽

pp. 146-161

Author(s):

Rodrigo Porteiro ◽

Sergio Nesmachnow ◽

Luis Hernández-Callejo

Keyword(s):

Machine Learning ◽

Load Forecasting ◽

Short Term ◽

Short Term Load Forecasting

Download Full-text

Ensemble and Neural Network Machine Learning Models for Short-Term Load Forecasting of Open Cast Mining Companies

Electrotechnical Systems and Complexes ◽

10.18503/2311-8318-2021-3(52)-57-65 ◽

2021 ◽

pp. 57-65

Author(s):

Dmitry Antonenkov ◽

◽

Pavel Matrenin ◽

Keyword(s):

Neural Network ◽

Machine Learning ◽

Load Forecasting ◽

Learning Models ◽

Short Term ◽

Mining Companies ◽

Open Cast Mining ◽

Short Term Load Forecasting ◽

Open Cast ◽

Machine Learning Models

Download Full-text

Review of Machine Learning for Short Term Load Forecasting

10.1007/978-981-16-7210-1_17 ◽

2021 ◽

pp. 180-190

Author(s):

Aijia Ding ◽

Huifen Chen ◽

Tingzhang Liu

Keyword(s):

Machine Learning ◽

Load Forecasting ◽

Short Term ◽

Short Term Load Forecasting

Download Full-text

Short-Term Load Forecasting for Electric Vehicle Charging Stations Based on Deep Learning Approaches

Applied Sciences ◽

10.3390/app9091723 ◽

2019 ◽

Vol 9 (9) ◽

pp. 1723 ◽

Cited By ~ 12

Author(s):

Juncheng Zhu ◽

Zhile Yang ◽

Yuanjun Guo ◽

Jiankang Zhang ◽

Huikun Yang

Keyword(s):

Deep Learning ◽

Power Systems ◽

Unit Commitment ◽

Combustion Engine ◽

Load Forecasting ◽

Learning Approaches ◽

Short Term ◽

Load Curve ◽

Short Term Load Forecasting ◽

Gated Recurrent Units

Short-term load forecasting is a key task to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to the unit commitment and economic load dispatch. In recent years, the boost of internal combustion engine (ICE) based vehicles leads to the fossil fuel shortage and environmental pollution, bringing significant contributions to the greenhouse gas emissions. One of the effective ways to solve problems is to use electric vehicles (EVs) to replace the ICE based vehicles. However, the mass rollout of EVs may cause severe problems to the power system due to the huge charging power and stochastic charging behaviors of the EVs drivers. The accurate model of EV charging load forecasting is, therefore, an emerging topic. In this paper, four featured deep learning approaches are employed and compared in forecasting the EVs charging load from the charging station perspective. Numerical results show that the gated recurrent units (GRU) model obtains the best performance on the hourly based historical data charging scenarios, and it, therefore, provides a useful tool of higher accuracy in terms of the hourly based short-term EVs load forecasting.

Download Full-text