A hybrid model for building energy consumption forecasting using long short term memory networks

Copyright © 2020 調和系工学研究室 - 北海道大学大学院情報科学研究院情報理工学部門複合情報工学分野 – All rights reserved.
論文紹介
A hybrid model for building energy consumption
forecasting using long short term memory networks
北海道大学大学院情報科学研究院
情報理工学部門複合情報工学分野調和系工学研究室
劉兆邦
2021年６月30日

• 著者
– Nivethitha Somu, Gauthama Raman M R, Krithi
Ramamrithama
• 発表
– Applied Energy Volume 261, 1 March 2020, 114131
• 論文リンク
– https://www.sciencedirect.com/science/article/pii/S03062619
19318185?via%3Dihub
• コード
Paper information 2

 eDemand: an energy consumption forecasting model
which employs long short term memory networks
and improved sine cosine optimization algorithm
(ISCOA-LSTM) for building energy consumption
forecasting
 Outperforms the state-of-the-art energy consumption
forecast models in terms of MAE,MAPE,MSE, RMS,
and Theil statistics.
Abstract 3

 Energy demand management has become an important
research area due to the shortage of energy resources,
ever-increasing global energy demand.
 non-linear, non-stationary and multi-seasonality nature.
weather conditions (indoor and outdoor), building
context and dynamics, time, occupancy, etc
Background 4

eDemand – Architecture
 Data acquisition and storage layer
 Data pre-processing layer
 Data analytics layer
 Application layer
Energy consumption forecasting model 5

LSTM
 RNN performs better than traditional model
(autoregressive moving average model)
 LSTM can solve the “vanishing and exploding gradient
problem”
Sine Cosine Optimization Algorithm(SCOA)
1. Inherent benefits from high exploration and avoid
trap at local optimal based on a set of random
candidate solutions and intensive search space with
simple sine and cosine functions
2. adaptive range of SCOA makes it to switch from
exploration [<1,>1] to exploitation [−1,1] using
simple sine and cosine functions
Why LSTM and ISCOA 6

Sine cosine optimization algorithm 7
Sine Cosine Optimization Algorithm (SCOA) is a population
based meta heuristic algorithm proposed by Seyedali Mirjalili
that uses simple sine and cosine mathematical operators for
solving optimization problems

• r1 is responsible for determining the next search
region to be explored
• r2 defines the direction of movement towards or away
from the best solution and lies in the range [0, 2pi]
• r3 is a random weight that stochastically emphasizes
or deemphasizes the effect of destination on the
current movement
• r4 is a random number in the range of [0,1], that
balance between the exploration and exploitation of
the search space, by switching between the sine and
cosine functions

ISCOA-LSTM
 Encoding strategy, i.e., generation of population
 Hyperparameter optimization
 Population updation, i.e., update the position of each
population using Haar wavelet based mutation
operator and
 Performance evaluation of ISCOA-LSTM
ISCOA-LSTM 10

• Encoding strategy
SCOA: populations were generated randomly
within a specified range ([Lowerlimit,
Upperlimit])
ISCOA: a vector encoding strategy for the
generation of initial population each with a
unique range
ISCOA-LSTM 11

Energy consumption forecasting model 12

DATA 13
Data: Kanwal Rekhi School of Information Technology
(KReSIT), an academic building in Indian Institute of
Technology (IIT), Mumbai, Indian

• Long term(LT) forecasting – Energy demand over a number of
years
• Mid term(MT) forecasting – Energy demand for weeks to months
Summer, Winter, Monsoon
• Short term(ST) forecasting – Energy demand for days or weeks
Summer, Winter, Monsoon
Output is every day’s energy consumption
Experiments 14

• Demonstrate the improvement of ISCOA-LSTM over
the considered data-driven approaches(MAE,MAPE
and so on are used to evaluate how the model
performs, lower value is better )
Experiments 15

Experiments 16
For long term
forecasting, MAE
of ISCOA-LSTM is
the lowest

Experiments 17

• ISCOA can used to find optimal hyperparameters
(learning rate, weight decay, momentum and
number of hidden layers) in LSTM
• ISCOA-LSTM provides accurate and reliable energy
demand predictions for efficient energy planning,
management, and conservation
Conclusion 18

A hybrid model for building energy consumption forecasting using long short term memory networks

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