scholarly journals A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 443 ◽  
Author(s):  
Sungwoo Park ◽  
Jihoon Moon ◽  
Seungwon Jung ◽  
Seungmin Rho ◽  
Sung Wook Baik ◽  
...  
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Load Forecasting ◽  
Gradient Boosting ◽  
Generation Process ◽  
Two Stage ◽  
Power Scheduling ◽  
Extreme Gradient Boosting ◽  
Random Forest Models ◽  
Combined Cooling

Smart grid systems, which have gained much attention due to its ability to reduce operation and management costs of power systems, consist of diverse components including energy storage, renewable energy, and combined cooling, heating and power (CCHP) systems. The CCHP has been investigated to reduce energy costs by using the thermal energy generated during the power generation process. For efficient utilization of CCHP and numerous power generation systems, accurate short-term load forecasting (STLF) is necessary. So far, even though many single algorithm-based STLF models have been proposed, they showed limited success in terms of applicability and coverage. This problem can be alleviated by combining such single algorithm-based models in ways that take advantage of their strengths. In this paper, we propose a novel two-stage STLF scheme; extreme gradient boosting and random forest models are executed in the first stage, and deep neural networks are executed in the second stage to combine them. To show the effectiveness of our proposed scheme, we compare our model with other popular single algorithm-based forecasting models and then show how much electric charges can be saved by operating CCHP based on the schedules made by the economic analysis on the predicted electric loads.

scholarly journals Environmentally Constrained Optimal Dispatch Method for Combined Cooling, Heating, and Power Systems Using Two-Stage Optimization

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4135
Author(s):  
Haesung Jo ◽  
Jaemin Park ◽  
Insu Kim
Keyword(s):  
Power Generation ◽  
Energy Efficiency ◽  
Power Systems ◽  
Cost Savings ◽  
Two Stage ◽  
Energy Mix ◽  
Optimal Dispatch ◽  
Study Results ◽  
Combined Cooling ◽  
The Impact

The reliance on coal-fired power generation has gradually reduced with the growing interest in the environment and safety, and the environmental effects of power generation are now being considered. However, it can be difficult to provide stable power to end-users while minimizing environmental pollution by replacing coal-fired systems with combined cooling, heat, and power (CCHP) systems that use natural gas, because CCHP systems have various power output vulnerabilities. Therefore, purchasing power from external electric grids is essential in areas where CCHP systems are built; hence, optimal CCHP controls should also consider energy purchased from external grids. This study proposes a two-stage algorithm to optimally control CCHP systems. In Stage One, the optimal energy mix using the Lagrange multiplier method for state-wide grids from which CCHP systems purchase deficient electricity was calculated. In Stage Two, the purchased volumes from these grids were used as inputs to the proposed optimization algorithm to optimize CCHP systems suitable for metropolitan areas. We used case studies to identify the accurate energy efficiency, costs, and minimal emissions. We chose the Atlanta area to analyze the CCHP system’s impact on energy efficiency, cost variation, and emission savings. Then, we calculated an energy mix suitable for the region for each simulation period. The case study results confirm that deploying an optimized CCHP system can reduce purchased volumes from the grid while reducing total emissions. We also analyzed the impact of the CCHP system on emissions and cost savings.

scholarly journals XGBoost-Based Day-Ahead Load Forecasting Algorithm Considering Behind-the-Meter Solar PV Generation

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 128
Author(s):  
Dong-Jin Bae ◽  
Bo-Sung Kwon ◽  
Kyung-Bin Song
Keyword(s):  
Load Forecasting ◽  
Rapid Expansion ◽  
Gradient Boosting ◽  
Base Temperature ◽  
Electric Load ◽  
Solar Pv ◽  
Model Case ◽  
Extreme Gradient Boosting ◽  
Pv Generation ◽  
The Impact

With the rapid expansion of renewable energy, the penetration rate of behind-the-meter (BTM) solar photovoltaic (PV) generators is increasing in South Korea. The BTM solar PV generation is not metered in real-time, distorts the electric load and increases the errors of load forecasting. In order to overcome the problems caused by the impact of BTM solar PV generation, an extreme gradient boosting (XGBoost) load forecasting algorithm is proposed. The capacity of the BTM solar PV generators is estimated based on an investigation of the deviation of load using a grid search. The influence of external factors was considered by using the fluctuation of the load used by lighting appliances and data filtering based on base temperature, as a result, the capacity of the BTM solar PV generators is accurately estimated. The distortion of electric load is eliminated by the reconstituted load method that adds the estimated BTM solar PV generation to the electric load, and the load forecasting is conducted using the XGBoost model. Case studies are performed to demonstrate the accuracy of prediction for the proposed method. The accuracy of the proposed algorithm was improved by 21% and 29% in 2019 and 2020, respectively, compared with the MAPE of the LSTM model that does not reflect the impact of BTM solar PV.

scholarly journals Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5193
Author(s):  
Nasir Ayub ◽  
Muhammad Irfan ◽  
Muhammad Awais ◽  
Usman Ali ◽  
Tariq Ali ◽  
...  
Keyword(s):  
Feature Selection ◽  
Load Forecasting ◽  
Energy Generation ◽  
Supervised Machine Learning ◽  
Gradient Boosting ◽  
Support Vector ◽  
Hybrid Techniques ◽  
Extreme Gradient Boosting ◽  
Electricity Load ◽  
Electricity Load Forecasting

Electrical load forecasting provides knowledge about future consumption and generation of electricity. There is a high level of fluctuation behavior between energy generation and consumption. Sometimes, the energy demand of the consumer becomes higher than the energy already generated, and vice versa. Electricity load forecasting provides a monitoring framework for future energy generation, consumption, and making a balance between them. In this paper, we propose a framework, in which deep learning and supervised machine learning techniques are implemented for electricity-load forecasting. A three-step model is proposed, which includes: feature selection, extraction, and classification. The hybrid of Random Forest (RF) and Extreme Gradient Boosting (XGB) is used to calculate features’ importance. The average feature importance of hybrid techniques selects the most relevant and high importance features in the feature selection method. The Recursive Feature Elimination (RFE) method is used to eliminate the irrelevant features in the feature extraction method. The load forecasting is performed with Support Vector Machines (SVM) and a hybrid of Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN). The meta-heuristic algorithms, i.e., Grey Wolf Optimization (GWO) and Earth Worm Optimization (EWO) are applied to tune the hyper-parameters of SVM and CNN-GRU, respectively. The accuracy of our enhanced techniques CNN-GRU-EWO and SVM-GWO is 96.33% and 90.67%, respectively. Our proposed techniques CNN-GRU-EWO and SVM-GWO perform 7% and 3% better than the State-Of-The-Art (SOTA). In the end, a comparison with SOTA techniques is performed to show the improvement of the proposed techniques. This comparison showed that the proposed technique performs well and results in the lowest performance error rates and highest accuracy rates as compared to other techniques.

scholarly journals Power Optimization for Wind Turbines Based on Stacking Model and Pitch Angle Adjustment

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4158 ◽  
Author(s):  
Zhikun Luo ◽  
Zhifeng Sun ◽  
Fengli Ma ◽  
Yihan Qin ◽  
Shihao Ma
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Wind Turbines ◽  
Power Output ◽  
Pitch Angle ◽  
Power Optimization ◽  
Wind Power Generation ◽  
Gradient Boosting ◽  
Light Gradient ◽  
Extreme Gradient Boosting

As we know, power optimization for wind turbines has great significance in the area of wind power generation, which means to make use of wind resources more efficiently. Especially nowadays, wind power generation has become more and more important. Generally speaking, many parameters could be optimized to enhance power output, including blade pitch angle, which is usually ignored. In this article, a stacking model composed of Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBOOST) and Light Gradient Boosting Machine (LGBM) is trained based on historical data exported from the Supervisory Control and Data Acquisition (SCADA) system for output power prediction. Then, we carry out power optimization through pitch angle adjustment based on the obtained prediction model. Our research results indicate that power output could be enhanced by adjusting pitch angle appropriately.

scholarly journals Short-Term Electricity Load Forecasting with Machine Learning

Information ◽  
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.

Optimizing backup power systems through Six Sigma

2014 ◽  
Vol 5 (2) ◽  
pp. 168-192 ◽  
Author(s):  
Bikram Jit Singh ◽  
Yash Bakshi
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Six Sigma ◽  
Generation Process ◽  
Content Type ◽  
Energy Scenario ◽  
Generation Cost ◽  
Future Challenges ◽  
Current Scenario ◽  
Energy Reform

Purpose – The aim of this paper is to follow an analytic-deductive approach to realize the issues related to the present power crisis and growing demand of backup power systems (BPSs), especially in India. It not only explores the need of high-cost BPSs in the current scenario but also puts an emphasis to reduce the running cost through parametric optimizations. Design/methodology/approach – Literature review identifies dependency of every sector on BPS and also highlights present and future challenges erupting in this age of power scarcity. Different improvement initiatives being used to improve efficiency of BPS have been studied, but quite rare application of Six Sigma in BPS sector, in actual was found. A define-measure-analyze-improve-control (DMAIC) model of Six Sigma is being suggested and practiced for bringing a breakthrough in efficiency or mileage of a diesel genset (used as a BPS). Findings – Importance of BPS cannot be ignored, particularly in developing countries, but feeble effort was seen to improve its power generation cost. An imprecise and outdated framework used for bringing a change in the BPS sector is unable to increase its overall equipment efficiency. The diversified methodology of Six Sigma has been successfully implemented through a case study discussed and further validates the approach adopted to tackle the problem formulated further in the paper. Research limitations/implications – The whole work more or less revolves around the Indian energy scenario and seems to be talking about the power generation process and its optimization, in context of only diesel genets as BPS. Originality/value – The paper outlines the need for energy reform in industry. The DMAIC approach of Six Sigma in the BPS sector is too infrequent and next, motivates to reduce BPS cost and its associated expenditures, which are generally ignored.

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