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Building air-conditioning and mechanical ventilation (ACMV) systems are responsible for significant energy consumption and yet, dissatisfaction with the thermal environment is prevalent among the occupants, revealing a widespread disparity between energy-efficiency and indoor thermal-comfort in buildings. This paper presents an indoor-climate control framework that bridges this gap between energy and comfort. The framework comprises two main components: a thermal-comfort prediction model,...

Accurate and reliable building energy predictions can bring significant benefits for energy conservations. With the development in smart buildings, massive amounts of building operational data are being collected and available for analysis. It is desired to develop big data-driven methods to fully realize the potential of building operational data in energy predictions. This paper investigates the usefulness of advanced recurrent neural network-based strategies for building energy...

Heating, Ventilation, and Air Conditioning (HVAC) is extremely energy-consuming, accounting for 40% of total building energy consumption. Therefore, it is crucial to design some energy-efficient building thermal control policies which can reduce the energy consumption of HVAC while maintaining the comfort of the occupants. However, implementing such a policy is challenging, because it involves various influencing factors in a building environment, which are usually hard to model and may be...

The emerging Internet of Things (IoT) technology enables smart building management and operation to improve building energy efficiency and occupant thermal comfort. In this paper, we perform data analysis using the IoT generated building data to derive accurate thermal comfort model for smart building control. Deep neural network (DNN) is used to model the relationship between the controllable building operations and thermal comfort. As thermal comfort is determined by multiple comfort...

It is important to create comfortable indoor environments for building occupants. This study developed artificial neural network (ANN) models for predicting thermal comfort in indoor environments by using thermal sensations and occupants’ behavior. The models were trained by data on air temperature, relative humidity, clothing insulation, metabolic rate, thermal sensations, and occupants’ behavior collected in ten offices and ten houses/apartments. The models were able to predict similar...

Livestock productivity is likely to be adversely affected by climate change mainly in terms of feed supply variations. Principal livestock food resources in Zambia and Malawi are grass areas, but very few data are available for supply management and amount estimation. The aim of this paper is to illustrate the procedure adopted for preliminary estimations of grassland biomass retrieval and grass growth cycle identification over a wide area between the Lukulu District and the Mongu District,...

Climate change has continued to impact negatively on water resources globally. For instance, extreme weather conditions especially the drought phenomena have become frequent in Africa. This has prompted water engineers and hydrologists to formulate mitigation and adaptation measures to address these challenges. The frequency of drought event of a defined severity for a defined return period is fundamental in planning, designing, operating and managing water resources systems within a basin....

Aiming at the prediction control for indoor temperature time-delay in variable air volume (VAV) air conditioning system, this paper presents an indoor temperature prediction control method based on Elman neural network multi-step prediction model. Firstly, this paper introduces basic control principles of pressure-dependent and pressure-independent VAV terminal through comparable analysis and points out significance of indoor temperature prediction control based on pressure-dependent VAV...

The heating energy demand stated in energy performance certificates (EPC) and in other instruments used in the of evaluation of building’s energy performance is usually determined assuming very specific (reference) indoor behavioral/heating patterns. Particularly, they tend to assume that households heat (nearly) the entire house to a “comfort” temperature during (nearly) all the heating season. However, several field studies have shown that there are major niches of the housing stock which...

Energy optimization in buildings by controlling the Heating Ventilation and Air Conditioning (HVAC) system is being researched extensively. In this paper, a model-free actor-critic Reinforcement Learning (RL) controller is designed using a variant of artificial recurrent neural networks called Long-Short-Term Memory (LSTM) networks. Optimization of thermal comfort alongside energy consumption is the goal in tuning this RL controller. The test platform, our office space, is designed using...

Energy optimization in buildings by controlling the Heating Ventilation and Air Conditioning (HVAC) system is being researched extensively. In this paper, a model-free actor-critic Reinforcement Learning (RL) controller is designed using a variant of artificial recurrent neural networks called Long-Short-Term Memory (LSTM) networks. Optimization of thermal comfort alongside energy consumption is the goal in tuning this RL controller. The test platform, our office space, is designed using...

Recent increase in green-roof installation has increased irrigation water consumption which could be wasteful using conventional watering management protocol. The knowledge gap in irrigation optimization to achieve water conservation could be filled. The complicated conventional approach uses weather and soil sensors to calculate watering needs, which is impractical and not cost-effective. This study employs artificial intelligence algorithms composed of artificial neural network and fuzzy...

The prediction of the air temperature (IT) and relative humidity (IH) in a building can help to reduce energy consumption for air conditioning. The purpose of this work was to apply the artificial neural network (ANNs) for an hourly prediction, 24–672h in advance of (IT) and (IH) in buildings found in hot-humid region. The inputs used in the model are 12 last values of indoor and outdoor air temperature and relative humidity. The experimental building is built with cement hallow block in...

Conventional chilled water based air conditioning systems use low temperature chilled water to remove both sensible load and latent load in conditioned space, and reheating devices are usually installed to warm the overcooled air, which leads to energy waste. Alternatively, this paper proposes a neural network (NN) model based predictive control strategy for simultaneous control of indoor air temperature and humidity by varying the speeds of compressor and supply air fan in a chilled water...