Energy
Smart building, Energy saving, DX

Energy management technology for smart building
Department of Information Systems Engineering, Graduate School of Information Science and Technology
Associate Professor Ittetsu Taniguchi
https://researchmap.jp/ittetsu?lang=en

Abstract
Most part of the social energy consumption is dominated bybuildings, and energy saving of the buildings becomes crucial.
Recently, buildings are often equipped with solar panels and batteries, and the energy management technology that manages theentire building, including various energy-consuming devices suchas air conditioning and smart home appliances, is becoming more
and more important. Prediction and optimization are key core technologies for reducing energy consumption without compromisingresidents’ convenience and comfort. Our research group is developing comprehensive energy management technology based onsuch prediction and optimization. Especially, we are developing
technology for predicting electricity prices and electricity demandusing deep learning, and efficient optimization technology for manyenergy-consuming devices.

▍ Background & Results

EnergyOur energy management technologies enable not only energy
saving, but also extracting demand-side flexibility. Our technologies contribute to the new energy services and expect to drive DX(Digital Transformation) in the energy sector.

forecasting accuracy over the leading-edge method at that time.
Our research group has developed novel energy management
technology based on such forecasting and optimization. We arealso actively implementing our technology in building equipment
such as air conditioning for industrial applications.

Fig. 1 Overview of proposed scheduling framework▍ Significance of the research and Future perspective
Buildings are the foundation of our daily lives and consume a lotof energy every day. To reduce energy consumption in buildings
without compromising the convenience and comfort of residents, itis necessary to properly operate energy-consuming devices suchas air conditioning and smart home appliances, in addition to solar
panels and storage batteries that have become popular in recentyears. Especially, it is necessary to optimize the entire system byutilizing predictive information such as the amount of renewableenergy generation by solar panels, residents’ behavior patterns,
and electricity prices.
Battery and energy-consuming equipment scheduling is an
important challenge to minimizing energy consumption. Usually,energy-consuming equipment such as air conditioning and smart
home appliances require hours to day planning period, while thegranularity of the renewable energy generation and the battery
behavior is seconds’ order. The scheduling problem is quite complicated and hard to solve. In this research, we have developed anovel hierarchical scheduling method. The proposed method has
reduced more than 48% in electricity costs in about 10 minutes ofcomputation time over the baseline method.
Time-series forecasting of electricity demand and electricity prices is another important challenge, and deep learning is often used
to tackle this. Normally, the time-series data of electricity demandand electricity prices contains noise, and it is known that the noiseaffects forecasting accuracy. In this research, we have researched
the novel forecasting method of electricity price by combining frequency analysis and deep learning. Our method achieved the best

Fig. 2 Example of scheduling results of air conditioning considering thermal comfort

Fig. 3 Overview of electricity price forecasting based on frequency analysis anddeep learning

P a t e n t EP3767559A1, US2021011439A1
Watari, Daichi; Taniguchi, Ittetsu; Goverde, Hans et al. Multi-time scale energy management framework for smart PV systems mixing fast and slowdynamics. Applied Energy. 2021, Volume 289, 116671, doi:10.1016/j.apenergy.2021.116671
Tr e a t i s e
Iwabuchi, Koki; Kato, Kenshiro; Watari, Daichi et al. Flexible electricity price forecasting by switching mother wavelets based on wavelet transform andlong short-term memory. Energy and AI. 2022, Volume 10, 100192, doi: 10.1016/j.egyai.2022.100192
URL
K e y w o r d smart building, energy management system, battery, smart appliance, solar energy