A Machine Learning Based Approach for Energy Consumption Forecasting in K-12 Schools

2020 ◽  
Author(s):  
Ganesh Doiphode ◽  
Hamidreza Najafi
Keyword(s):  
Neural Network ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Network Model ◽  
Neural Network Model ◽  
Accurate Estimation ◽  
School Buildings ◽  
Climate Zone ◽  
Brevard County ◽  
K 12

Abstract Energy costs are the second highest operational expense for K-12 schools in the United States. Improving energy efficiency and moving towards sustainable school buildings not only result in substantial cost savings and reduction of environmental emissions, but also provides an opportunity to enhance students’ awareness regarding energy, environment, and sustainability. Effective tools and techniques that provide thorough understanding of energy consumption in school buildings are valuable to school districts by helping them with prioritizing energy efficiency projects. In the present paper, a multi-layer perceptron (MLP) neural network model is developed for estimating monthly energy consumption of K-12 schools in Brevard County, Florida. The inputs to the network are considered as number of occupants, days of operation per months, building’s area, average monthly outdoor dry-bulb temperature and relative humidity, as well as the month’s number and the output from the network is monthly energy consumption. Various network topologies are considered and tested to achieve the optimal configuration for the network. The selected network is successfully trained using three years of energy consumption data for 25 schools in Brevard County, FL (high schools, middle schools, and elementary schools). The results showed that the developed neural network model is capable of accurate estimation of monthly energy consumption of schools. The network tested and validated using the data from schools which were not included in the training dataset and the errors between the known values and estimated values for monthly energy consumptions are evaluated and discussed. Although the current study covers one particular school district (Brevard county) in a given climate zone (2a-hot and humid), the developed approach can be extended to incorporate various climate zones and serve as an effective tool for school energy conservation managers. The end user may obtain a clear idea of the energy consumption of the school building and how it compares against other buildings within the same category and climate zone, with minimum input data required.

Week-ahead Forecasting of Household Energy Consumption Using CNN and Multivariate Data

2021 ◽  
Vol 20 ◽  
pp. 182-188
Author(s):  
Vanita Agrawal ◽  
Pradyut K. Goswami ◽  
Kandarpa K. Sarma
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Power System ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Smart Grids ◽  
Smart Meters ◽  
Household Energy ◽  
Household Energy Consumption

Short-Term Load Forecasting for buildings has gained a lot of importance in recent times due to the ongoing penetration of renewable energy and the upgradation of power system networks to Smart Grids embedded with smart meters. Power System expansion is not able to keep pace with the energy consumption demands. In this scenario, accurate household energy forecasting is one of the key solutions to managing the demand side energy. Even a small percentage of improvement in forecasting error, translates to a lot of saving for both producers and consumers. In this paper, it was found out that Aggregated 1-Dimensional Convolutional Neural Networks can be effectively modeled to predict the household consumption with greater accuracy than a basic 1-Dimensional Convolutional Neural Network model or a classical Auto Regressive Integrated Moving Average model. The proposed Aggregated Convolutional Neural Network model was tested on a 4 year household energy consumption dataset and gave very promising Root Mean Square Error reduction

Prediction of Airport Energy Consumption Using a Hybrid Grey Neural Network Model

2012 ◽  
Vol 608-609 ◽  
pp. 1252-1256 ◽  
Author(s):  
Jing Jie Chen ◽  
Chen Xiao ◽  
Wen Gao Qian
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Civil Aviation ◽  
Aviation Industry ◽  
Consumption Data ◽  
Prediction And Control ◽  
And Control

Prediction and control of airport energy consumption plays an important role in promoting energy saving and emission reduction in the civil aviation industry. In view of the complexity and nonlinearity of energy consumption system, as well as a small number of airport energy consumption data, this study develops a hybrid grey neural network model, which organically combines GM (1, 1) model and BP neural network in parallel and series connections, on the basis of analysis of main prediction methods. With energy consumption data from one Chinese airport for the whole year 2010, this study analyzes and compares different prediction results using different models through matlab. It shows that the hybrid model has a better accurate prediction, and its prediction accuracy can be controlled within 7%.

Neural Network-Based Risk Assessment of Artificial Fill Slope in Residential Urban Region

2010 ◽  
Vol 5 (6) ◽  
pp. 642-649
Author(s):  
Toshitaka Kamai ◽  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Urban Areas ◽  
Slope Instability ◽  
Urban Region ◽  
Accurate Estimation ◽  
Hazard Mapping ◽  
Earthquake Parameters ◽  
Fill Slope

Destructive urban earthquakes have triggered landslides on gentle residential slopes in Japan. Earthquake-induced slope instability is closely related to artificial landforms, especially valley fill (embankments). The study of artificial landform changes has shown that differences in fill shape, such as depth, width, base angle inclination, and cross-sectional form, may be key discriminating factors in slope instability. The earthquake trigger mechanism must be considered in accurate estimation analysis, but it is difficult to include earthquake parameters in convenient linear multivariate analysis. Neural network analysis is applied to assess large fill slope instability in residential urban areas. The neural network model we developed including causative - fill shape, groundwater, and construction age - and triggering factors - distance from the fault, moment magnitude, and direction to the fault - was checked independently against another dataset and sensitivity was analyzed. Our proposed neural network model should enable us to establish more reliable landslide hazard mapping in residential urban areas, which, in turn, should aid disaster resilient societies in seismically active regions.

Estimation on State of Charge of Power Battery Based on the Grey Neural Network Model

2013 ◽  
Vol 427-429 ◽  
pp. 1158-1162 ◽  
Author(s):  
Lu Lu Du ◽  
Bei Li ◽  
Huai De Zhang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Discharge Current ◽  
Vital Role ◽  
State Of Charge ◽  
Accurate Estimation ◽  
Battery Life ◽  
Discharge Process ◽  
Power Battery

The state of charge (SOC) of power battery is an important parameter of battery state, and it plays a vital role in real-time accurate estimation, condition monitoring, improving battery life, and ensuring the safety of power supply. This paper presents the grey neural network model of the relation between the battery SOC and rebound voltage, discharge current. Based on this model, a new on-line SOC detection method using rebound voltage and discharge current in the discharge process is proposed. From the testing results, the model and algorithm were proved to be feasible and effective, and the estimated error is controlled within a range of ±8%.

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