Improving Indoor Environmental Quality (IEQ) and Comfortability of Lecture Rooms: Case Study – UniCITI Alam Campus, Padang Besar, Malaysia

Naamandadin, Nurul Akmam; Noor, Siti Nur Aishah Mohd; Zainol, Norrazman Zaiha; Awang, Arita Hanim; Rahman, Syakir Amir Abdul; Mohamad, Khairunnisa; Ahmad, Muhammad Naim

doi:10.1088/1757-899x/743/1/012027

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…The power generated by PV is assumed as a linear function of the solar irradiance [56], described as…”

Section: B Heat Pump and Hot Water Tankmentioning

confidence: 99%

A Learning-Based Model Predictive Control Strategy for Home Energy Management Systems

Cai,

Sawant,

Reinhardt

et al. 2023

IEEE Access

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This paper presents a model predictive control (MPC)-based reinforcement learning (RL) approach for a home energy management system (HEMS). The house consists of an air-to-water heat pump connected to a hot water tank that supplies thermal energy to a water-based floor heating system. Additionally, it includes a photovoltaic (PV) array and a battery storage system. The HEMS is supposed to exploit the house thermal inertia and battery storage to shift demand from peak hours to off-peak periods and earn benefits by selling excess energy to the utility grid during periods of high electricity prices. However, designing such a HEMS is challenging because the discrepancies due to model mismatch make erroneous predictions of the system dynamics, leading to a non-optimal decision making. Besides, uncertainties in the house thermodynamics, misprediction in the forecasting of PV generation, outdoor temperature, and user load demand make the problem more challenging. We solve this issue by approximating the optimal policy by a parameterized MPC scheme and updating the parameters via a compatible delayed deterministic actor-critic (with gradient Q-learning critic, i.e., CDDAC-GQ) algorithm. Simulation results show that the proposed MPC-based RL HEMS can effectively deliver a policy that satisfies both indoor thermal comfort and economic costs even in the case of inaccurate model and system uncertainties. Furthermore, we conduct a thorough comparison between the CDDAC-GQ algorithm and the conventional twin delayed deep deterministic policy gradient (TD3) algorithm, the results of which affirm the efficacy of our proposed method in addressing complex HEMS problems.INDEX TERMS Model predictive control (MPC), reinforcement learning (RL), home energy management system (HEMS), inaccurate model, system uncertainties. Nomenclature

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“…The power generated by PV is assumed as a linear function of the solar irradiance [56], described as…”

Section: B Heat Pump and Hot Water Tankmentioning

confidence: 99%

A Learning-Based Model Predictive Control Strategy for Home Energy Management Systems

Cai,

Sawant,

Reinhardt

et al. 2023

IEEE Access

View full text Add to dashboard Cite

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“…The radiant solar energy exerted by the sun is the integration of the sun radiation for a given time period named as solar irradiance or solar insolation. The solar irradiance can be calculated by measurement of the light energy falls on the earth surface in area of square meter in a second (Naamandadin et al, 2018). The measurement instrument of the solar radiation is names as Pyrometer.…”

Section: Introductionmentioning

confidence: 99%

Partial Shading Effect on Power Generation of BIPV System in Bangladesh

Jahangir,

Mia,

Chowdhury

2023

IJRER

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The research tries to observe the power difference for the PV module in the case of the partial and void of partial shading in Bangladesh. For analysis of the partial shading effect on the PV module it is necessary to establish an experiment set up for the PV module. To investigate the power output from PV module the partial shading effect has been generated using the paper coverage. The experimental result shows that the photovoltaic module can generate up to 41% more power output in the case of the void of the shading effect rather than in the shading effect. Around 50% portion of the PV module has been shaded for making partial shading effect. The other important factors influencing the output power of the PV module are solar irradiance, cell temperature; ideality factor. To get the maximum output it is essential to avoid shading effect which will help to generate more power. In this paper, MATLAB simulation of PV module has been done showing the partial shading effect under consideration of various factors like series resistance, ideality factor, temperature. Partial shading effect has been created by varying the level of solar radiation. An experiment has been carried out to see the overall performance of PV module under shading effect at a fixed tilt angle.

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Improving Indoor Environmental Quality (IEQ) and Comfortability of Lecture Rooms: Case Study – UniCITI Alam Campus, Padang Besar, Malaysia

Cited by 2 publications

References 5 publications

A Learning-Based Model Predictive Control Strategy for Home Energy Management Systems

A Learning-Based Model Predictive Control Strategy for Home Energy Management Systems

Partial Shading Effect on Power Generation of BIPV System in Bangladesh

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