scholarly journals Model of Short-Term Forecast of Electrical Energy Consumption of Ural United Power System by Separating of a Maximal Similarity Sample into the Positive and Negative Levels

2017 ◽  
Vol 4 (3) ◽  
pp. 11-18
Author(s):  
T.S. Demyanenko ◽  
Keyword(s):  
Energy Consumption ◽  
Power System ◽  
Electrical Energy ◽  
Electrical Energy Consumption ◽  
Short Term ◽  
Short Term Forecast ◽  
Term Forecast ◽  
United Power System

scholarly journals HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2672 ◽  
Author(s):  
Ivana Kiprijanovska ◽  
Simon Stankoski ◽  
Igor Ilievski ◽  
Slobodan Jovanovski ◽  
Matjaž Gams ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Error Score ◽  
Load Forecasting ◽  
Electrical Energy ◽  
Electrical Energy Consumption ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Short Term ◽  
Benchmark Model ◽  
Short Term Load Forecasting

Short-term load forecasting is integral to the energy planning sector. Various techniques have been employed to achieve effective operation of power systems and efficient market management. We present a scalable system for day-ahead household electrical energy consumption forecasting, named HousEEC. The proposed forecasting method is based on a deep residual neural network, and integrates multiple sources of information by extracting features from (i) contextual data (weather, calendar), and (ii) the historical load of the particular household and all households present in the dataset. Additionally, we compute novel domain-specific time-series features that allow the system to better model the pattern of energy consumption of the household. The experimental analysis and evaluation were performed on one of the most extensive datasets for household electrical energy consumption, Pecan Street, containing almost four years of data. Multiple test cases show that the proposed model provides accurate load forecasting results, achieving a root-mean-square error score of 0.44 kWh and mean absolute error score of 0.23 kWh, for short-term load forecasting for 300 households. The analysis showed that, for hourly forecasting, our model had 8% error (22 kWh), which is 4 percentage points better than the benchmark model. The daily analysis showed that our model had 2% error (131 kWh), which is significantly less compared to the benchmark model, with 6% error (360 kWh).

scholarly journals Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1102 ◽  
Author(s):  
Kasım Zor ◽  
Özgür Çelik ◽  
Oğuzhan Timur ◽  
Ahmet Teke
Keyword(s):  
Gene Expression ◽  
Energy Consumption ◽  
Gene Expression Programming ◽  
Electrical Energy ◽  
Machine Learning Algorithms ◽  
Small Scale ◽  
Group Method ◽  
Electrical Energy Consumption ◽  
Short Term ◽  
Study Gene Expression

Over the past decade, energy forecasting applications not only on the grid side of electric power systems but also on the customer side for load and demand prediction purposes have become ubiquitous after the advancements in the smart grid technologies. Within this context, short-term electrical energy consumption forecasting is a requisite for energy management and planning of all buildings from households and residences in the small-scale to huge building complexes in the large-scale. Today’s popular machine learning algorithms in the literature are commonly used to forecast short-term building electrical energy consumption by generating an abstruse analytical expression between explanatory variables and response variables. In this study, gene expression programming (GEP) and group method of data handling (GMDH) networks are meticulously employed for creating genuine and easily understandable mathematical models among predictor variables and target variables and forecasting short-term electrical energy consumption, belonging to a large hospital complex situated in the Eastern Mediterranean. Consequently, acquired results yielded mean absolute percentage errors of 0.620% for GMDH networks and 0.641% for GEP models, which reveal that the forecasting process can be accomplished and formulated simultaneously via proposed algorithms without the need of applying feature selection methods.

scholarly journals Bisu Main Campus Electrical Energy Consumption: Basis For Improvement

2012 ◽  
Vol 9 (1) ◽  
Author(s):  
Ivy M. Bagsac ◽  
Roland Gabo ◽  
Teofanes Sarabosing ◽  
Dave Pojadas ◽  
Anacleta Perez ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Power System ◽  
Solar Power ◽  
Electrical Power ◽  
Electrical Energy ◽  
Electrical Energy Consumption ◽  
Electrical System ◽  
Electrical Power System ◽  
Main Campus ◽  
The University

 The research was conducted at the Bohol Island State University Main Campus. It aims to assess the status of the electrical power system of the university as well as determine the perceptions of the electrical experts on the satisfaction rating of the school’s electrical system. It was found out that the overall rating of the electrical system of BISU Main Campus is “fair”. This means that there are several aspects that need improvement such as the implementation of a maintenance program and the hiring of maintenance personnel. The researchers recommend that the personnel should not be the instructors themselves but designated electrical technologists must be hired. Furthermore, there should be a periodic inspection so that defects may be detected and given remedies the earliest time possible to avoid accidents. There should also be fund allotment that should be imposed for the maintenance and personnel. Furthermore, the university must purchase more electrical supplies, tools and equipment solely for electrical maintenance. There must also be a separate maintenance shop for maintenance purpose only. An alternative electrical power source should be employed by the university such as the solar power. Because of the very high and expensive electrical energy consumption, there is a need to use a more efficient alternative source and that is the solar power. Keywords - electrical power system, electrical energy consumption

A persuasive multi-agent simulator to improve electrical energy consumption

2021 ◽  
pp. 1-15
Author(s):  
Fernanda P. Mota ◽  
Cristiano R. Steffens ◽  
Diana F. Adamatti ◽  
Silvia S. Da C Botelho ◽  
Vagner Rosa
Keyword(s):  
Energy Consumption ◽  
Electrical Energy ◽  
Electrical Energy Consumption ◽  
Multi Agent
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