scholarly journals Low-cost and simple short-term load forecasting for energy management systems in small and middle-sized office buildings

2020 ◽  
pp. 014459871990096 ◽  
Author(s):  
Dongho Lee
Keyword(s):  
Energy Management ◽  
Low Cost ◽  
Load Forecasting ◽  
Office Buildings ◽  
Management Systems ◽  
Short Term ◽  
Energy Management Systems ◽  
Short Term Load Forecasting

Short-Term Load Forecasting Using Hybrid Neural Network

2021 ◽  
Vol 12 (1) ◽  
pp. 142-156
Author(s):  
Muhammad Nadeem ◽  
Muhammad Altaf ◽  
Ayaz Ahmad
Keyword(s):  
Neural Network ◽  
New England ◽  
Low Cost ◽  
Electrical Power ◽  
Load Forecasting ◽  
Electricity Consumption ◽  
Forecast Model ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Multiple Variables

One of the important factors in generating low cost electrical power is the accurate forecasting of electricity consumption called load forecasting. The major objective of the load forecasting is to trim down the error between actual load and forecasted load. Due to the nonlinear nature of load forecasting and its dependency on multiple variables, the traditional forecasting methods are normally outperformed by artificial intelligence techniques. In this research paper, a robust short term load forecasting technique for one to seven days ahead is introduced based on particle swarm optimization (PSO) and Levenberg Marquardt (LM) neural network forecast model, where the PSO and LM algorithm are used for the training process of neural network. The proposed methods are tested to predict the load of the New England Power Pool region's grid and compared with the existing techniques using mean absolute percentage errors to analyze the performance of the proposed methods. Forecast results confirm that the proposed LM and PSO-based neural network schemes outperformed the existing techniques.

Design of a Fuzzy Logic Controller for Short-Term Load Forecasting With Randomly Varying Load

2021 ◽  
Vol 13 (4) ◽  
pp. 32-49
Author(s):  
D. V. N. Ananth ◽  
Lagudu Venkata Suresh Kumar ◽  
Tulasichandra Sekhar Gorripotu ◽  
Ahmad Taher Azar
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Load Forecasting ◽  
Short Term ◽  
Random Load ◽  
Traditional System ◽  
Energy Management Systems ◽  
Load Demand ◽  
Short Term Load Forecasting ◽  
Changes Over Time

Short-term load forecasting (STLF) is an integral component of energy management systems. In this paper, fuzzy logic-based algorithm is used for short-term load forecasting. The load changes over time and the goal is to satisfy the shift in demand and to maintain a fault as low as possible between the reference and real powers. The error in the load demand in mega-watt (MW) is compared with proposed technique as well as conventional methods. Three cases were investigated in which the load changes were 1) more random in nature, but the variance to the reference was more; 2) the random load changes were simpler, but a little different from the reference; and lastly, 3) the load changing was random, and the reference deviation was maximum. The results are analyzed for different load changes, and the corresponding results are verified using MATLAB. The deviation of the error value in load response is less experienced with a fuzzy logic controller than with a traditional system, and in fewer iterations, the objective function is also achieved.

scholarly journals A Forecast-Based Load Management Approach for Commercial Buildings Demonstrated on an Integration of BEV

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3576
Author(s):  
Thomas Steens ◽  
Jan-Simon Telle ◽  
Benedikt Hanke ◽  
Karsten von Maydell ◽  
Carsten Agert ◽  
...  
Keyword(s):  
Deep Learning ◽  
Energy Management ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Management Systems ◽  
Management Approach ◽  
Feed Forward Neural Network ◽  
Learning Methods ◽  
Existing Building ◽  
Energy Management Systems

Load-forecasting problems have already been widely addressed with different approaches, granularities and objectives. Recent studies focused not only on deep learning methods but also on forecasting loads on single building level. This study aims to research problems and possibilities arising by using different load-forecasting techniques to manage loads. For that behavior of two neural networks, Long Short-Term Memory and Feed-Forward Neural Network as well as two statistical methods, standardized load profiles and personalized standardized load profiles are analyzed and assessed by using a sliding-window forecast approach. The results show that personalized standardized load profiles (MAE: 3.99) can perform similar to deep learning methods (for example, LSTM MAE: 4.47). However, because of the simplistic approach, load profiles are not able to adapt to new patterns. As a case study for evaluating the support of load-forecasting for applications in energy management systems, the integration of charging stations into an existing building is simulated by using load-forecasts to schedule the charging procedures. It is shown that forecast- based controlled charging can have a significant impact by lowering overload peaks exceeding the house connection point power limit (controlled charging 20.24 kW; uncontrolled charging: 65.15 kW) while slightly increasing average charging duration. It is concluded that integration of high flexible loads can be supported by using forecast-based energy management systems with regards to their limitations.

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