Measurement and Verification (M&V) aims to quantify savings achieved as part of energy efficiency and energy management projects. M&V depends heavily on metered energy data, modelling parameters and uncertainties that govern the energy system under consideration. M&V therefore requires a stringent handle on the inherent uncertainties in the calculated savings. The Bayesian framework of data analysis in the form of non-parametric, nonlinear Gaussian Process (GP) regression provides a mechanism by which these uncertainties can be quantified thoroughly, and is therefore an attractive alternative to the more traditional frequentist approach. It is important to select appropriate kernels to construct the prior when performing GP regression. This paper aims to construct a guideline for a practical GP regression within the energy M&V framework. It does not attempt to quantify energy losses or savings, but rather presents a case study that could act as a road map for energy managers and M&V professionals to apply the GP regression as a Bayesian alternative to base-line adjustment. Special attention will be given to the selection of appropriate kernels for the application of baseline adjustment and energy savings quantification in a model-independent manner.

The proliferation of solar power systems could cause instability within existing power grids due to the variable nature of solar power. A well-defined statistical model is important for managing the supply-and-demand dynamics of a power system that contains a significant variable renewable energy component. It is furthermore important to consider the inherent uncertainty in the data when modeling such a complex power system. Gaussian process regression has the potential to address both of these concerns: the probabilistic modeling of solar radiation data could assist in managing the variability of solar power, as well as provide a mechanism to deal with uncertainty. In this paper, solar radiation data was obtained from the Southern African Universities Radiometric Network and used to train a Gaussian process regression model which was developed especially for this purpose. Attention was given to constructing an appropriate Gaussian process kernel. It was found that a carefully constructed kernel allowed for the successful interpolation of global horizontal irradiance data, with a root-mean-squared error of 82.2W/m2. Gaps in the data, due to possible meter failure, were also bridged by the Gaussian process with a root-mean-squared error of 94.1 W/m2 and accompanying confidence intervals. A root-mean-squared error of 151.1 W/m2 was found when forecasting the global horizontal irradiance with a forecasting horizon of five days. These results, achieved in modeling solar radiation data using Gaussian process regression, could open new avenues in the development of probabilistic renewable energy management systems. Such systems could aid smart grid operators and support energy trading platforms, by allowing for better-informed decisions that incorporate the inherent uncertainty of stochastic power systems.

The term decentralised as a description of the architecture, operation, and governance of permissionless blockchain systems has become ubiquitous. However, in these contexts, the term decentralised has no clear definition. Blockchain ecosystems are complex, and thus it is essential to address confusion among stakeholders about their nature and promote understanding of the intentions and consequences of their implementation. This article offers a theoretical definition of the term decentralised in the context of permissionless blockchain systems. It is proposed that five inextricable and interconnected aspects are required, at a minimum, to warrant a claim that a permissionless blockchain system is decentralised. These aspects are disintermediation, a peer-to-peer network, a distributed blockchain data structure, algorithmic trust, and open-source principles. The relationship between the five aspects is discussed, and it is argued that decentralisation is not binary but exists on a spectrum. Any variation in one or more aspects may impact the system’s decentralised nature as a whole. The researchers identify areas where further investigation in this field is required and propose instances where the knowledge garnered may be used.

The unique situation of utility power curtailment unveils opportunities in the fields of energy management and digital resource management. During utility load shedding events, campuses are typically driven as Photo Voltaic (PV)–diesel generator hybrid systems, of which the main fossil resource driver is diesel. With the appropriate Supervisory Control and Data Acquisition (SCADA) systems, discrete departmental energy policies along with control, forecasting and Internet of Things (IoT) infrastructure, the campus hybrid system could be optimized on a short timescale during the shedding event. In this paper the optimization methodology, required technology infrastructure, possible forecasting algorithms and potential implementation will be discussed.

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Consensus algorithms that function in permissionless blockchain systems must randomly select new block proposers in a decentralised environment. Our contribution is a new blockchain consensus algorithm called Proof-of-Publicly Verifiable Randomness (PoPVR). It may be used in blockchain design to make permissionless blockchain systems function as pseudo-random number generators and to use the results for decentralised consensus. The method employs verifiable random functions to embed pseudo-random number seeds in the blockchain that are confidential, tamper-resistant, unpredictable, collision-resistant, and publicly verifiable. PoPVR does not require large-scale computation, as is the case with Proof-of-Work and is not vulnerable to the exclusion of less wealthy stakeholders from the consensus process inherent in stakebased alternatives. It aims to promote fairness of participation in the consensus process by all participants and functions transparently using only open-source algorithms. PoPVR may also be useful in blockchain systems where asset values cannot be directly compared, for example, logistical systems, intellectual property records and the direct trading of commodities and services. PoPVR scales well with complexity linear in the number of transactions per block.INDEX TERMS Consensus algorithm, decentralised consensus, permissionless blockchain systems, proofbased consensus algorithms, proof-of-publicly verifiable randomness, pseudo-random number generation, random number seeds, verifiable random functions, vote-based consensus algorithms. JACQUES M. MARITZ received the master's degree and the Ph.D. degree in astrophysics from the University of the Free State, South Africa, in 2017.He has been a Lecturer in engineering sciences with the University of the Free State, since 2017. His research interests include physics, astrophysics, energy modeling, energy analytics, energy AI, and power systems.

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Many blockchain processes require pseudo-random numbers. This is especially true of blockchain consensus mechanisms that aim to fairly distribute the opportunity to propose new blocks between the participants in the system. The starting point for these processes is a source of randomness that participants cannot manipulate. This paper proposes two methods for embedding random number seeds in a blockchain data structure to serve as inputs to pseudo-random number generators. Because the output of a pseudo-random number generator depends deterministically on its seed, the properties of the seed are critical to the quality of the eventual pseudo-random number produced. Our protocol, B-Rand, embeds random number seeds that are confidential, tamper-resistant, unpredictable, collision-resistant, and publicly verifiable as part of every transaction. These seeds may then be used by transaction owners to participate in processes in the blockchain system that require pseudo-random numbers. Both the Single Secret and Double Secret B-Rand protocols are highly scalable with low space and computational cost, and the worst case is linear in the number of transactions per block.INDEX TERMS B-Rand, blockchain, consensus algorithm, homomorphic encryption, pseudo-random number generation, random number seeds.