A novel regression method in forecasting short-term grid electricity load in buildings that were connected to the smart grid

Ertuğrul, Ömer Faruk; Tekin, Hazret; Tekin, Ramazan

doi:10.1007/s00202-020-01114-3

Cited by 11 publications

(3 citation statements)

References 18 publications

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“…Different error measures are used in this study to evaluate the accuracy of the proposed methodology from a variety of perspectives [32]. One of the most popular error measurements, MSE, can be calculated as follows [33]:…”

Section: Resultsmentioning

confidence: 99%

Forecasting of daily dam occupancy rates using LSTM networks

Ayyıldız

Erdoğan²

2022

WJER

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Due to unconscious consumption of natural water resources and climate change, a water crisis is expected in the upcoming years. At this point, it is necessary to know the water levels in the dams and develop strategies for water-saving applications in the coming periods. This study aimed to propose the artificial neural network models for forecasting the water in the dams that provide usable water for the future. For this reason, long short-term memory (LSTM) networks that are a type of recurrent neural networks are employed to make future forecasts. The daily dam occupancy rate data between 2005 and 2021 for İstanbul is used to train the LSTM network. Then, the developed models are used to forecast over the next 30 days. The data are used in ARIMA to model the daily dam occupancy time series, for a fair comparison. The forecast values gained by the proposed LSTM network are compared with the traditional methods using RMSE and MAPE for all the forecast horizons. The results show that the LSTM-based forecast model always has a better accuracy rate than the ARIMA. Keywords: ANNs, dam, forecasting, occupancy, LSTM, RNNs;

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Section: Resultsmentioning

confidence: 99%

Forecasting of daily dam occupancy rates using LSTM networks

Ayyıldız

Erdoğan²

2022

WJER

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“…RMSE is another KPI that can evaluate forecasting models. The mathematical expression for RMSE is given [ 88 ]:

…”

Section: Proposed Methodologymentioning

confidence: 99%

Forecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chain

Ayyıldız

Erdoğan

Gümüş

2021

Computers in Biology and Medicine

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“…The new challenges are in the complexity and diversity of the load profiles at the different places in the grid hierarchy. The traditional approaches, based on multivariable linear regression (MLR) or time series (TS) models, i.e., ARMA and its variants, verified, mainly, for a higher level of aggregations, are, also, in use for smart-meter-based forecasting [ 1 ] or for energy management in buildings [ 2 ]. Simple and fast algorithms make them favorable solutions for real-time forecasting functionalities, as for, e.g., dynamic demand response, presented in [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Load Forecasting Methodology Based on Generalized Additive Model with Automatic Variable Selection

Krstonijević

2022

Sensors

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For decentralized energy management in a smart grid, there is a need for electric load forecasting at different places in the grid hierarchy and for different levels of aggregation. Load forecasting functionality relies on the load time series prediction model, which provides accurate forecasts. Complex and heterogeneous multi-source load time series in a smart grid require flexible modeling approaches to meet the accuracy demand. This work proposes an adaptive load forecasting methodology based on the generalized additive model (GAM) with the big data estimation method. It is based on a set of GAM terms, constructed for a specific multi-source load forecasting application in the grid and a procedure that dynamically selects the most relevant terms and generates forecasts for particular load time series. Data from publicly available New York Independent System Operator (NYISO) databases are used for testing. The 24-hour-ahead forecasting results for eleven New York City zones, of different sizes and types, indicate the applicability of the proposed methodology.

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A novel regression method in forecasting short-term grid electricity load in buildings that were connected to the smart grid

Cited by 11 publications

References 18 publications

Forecasting of daily dam occupancy rates using LSTM networks

Forecasting of daily dam occupancy rates using LSTM networks

Forecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chain

Adaptive Load Forecasting Methodology Based on Generalized Additive Model with Automatic Variable Selection

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